2025-08-01 02:09:40.734 | INFO     | yolox_microbt.core.trainer:before_train:88 - args: Namespace(config='configs.sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1', experiment_name='sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1', name=None, dist_backend='nccl', dist_url=None, batch_size=64, devices=8, exp_file=None, resume=False, ckpt=None, start_epoch=None, num_machines=1, machine_rank=0, fp16=False, cache=None, occupy=False, logger='tensorboard', opts=[])
2025-08-01 02:09:40.737 | 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     │ 10                                                      │
├───────────────────┼─────────────────────────────────────────────────────────┤
│ 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_trainset_sc1' │
├───────────────────┼─────────────────────────────────────────────────────────┤
│ test_size         │ (416, 416)                                              │
├───────────────────┼─────────────────────────────────────────────────────────┤
│ test_conf         │ 0.01                                                    │
├───────────────────┼─────────────────────────────────────────────────────────┤
│ nmsthre           │ 0.65                                                    │
├───────────────────┼─────────────────────────────────────────────────────────┤
│ qat_warmup_epoch  │ 0                                                       │
├───────────────────┼─────────────────────────────────────────────────────────┤
│ qat_clib_epoch    │ 2                                                       │
╘═══════════════════╧═════════════════════════════════════════════════════════╛
2025-08-01 02:09:41.585 | INFO     | yolox_microbt.core.trainer:before_train:129 - init prefetcher, this might take one minute or less...
2025-08-01 02:09:44.864 | INFO     | yolox_microbt.core.trainer:before_train:168 - Training start...
2025-08-01 02:09:44.973 | INFO     | yolox_microbt.core.trainer:before_train:169 - 
DistributedDataParallel(
  (module): YOLOXTrainer(
    (yolox): GraphModule(
      (backbone0): Module(
        (backbone): Module(
          (0): Module(
            (0): Module(
              (conv): ConvBnReLU2d(
                3, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
                (bn): BatchNorm2d(8, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=8, bias=False
                (bn): BatchNorm2d(8, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                8, 10, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(10, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                10, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(40, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                40, 40, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=40, bias=False
                (bn): BatchNorm2d(40, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                40, 8, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(8, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                8, 32, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False
                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                32, 8, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(8, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                8, 32, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False
                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                32, 10, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(10, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                10, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(40, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                40, 40, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=40, bias=False
                (bn): BatchNorm2d(40, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                40, 10, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(10, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                10, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(40, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                40, 40, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=40, bias=False
                (bn): BatchNorm2d(40, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                40, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False
                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                64, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False
                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                64, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False
                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                64, 48, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(48, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                48, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(192, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False
                (bn): BatchNorm2d(192, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                192, 52, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(52, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                52, 208, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(208, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                208, 208, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=208, bias=False
                (bn): BatchNorm2d(208, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                208, 88, kernel_size=(1, 1), stride=(1, 1), groups=2, bias=False
                (bn): BatchNorm2d(88, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                88, 352, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(352, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                352, 352, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=352, bias=False
                (bn): BatchNorm2d(352, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                352, 88, kernel_size=(1, 1), stride=(1, 1), groups=2, bias=False
                (bn): BatchNorm2d(88, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                88, 352, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(352, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBnReLU2d(
                352, 352, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=352, bias=False
                (bn): BatchNorm2d(352, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (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): ConvBn2d(
                352, 144, kernel_size=(1, 1), stride=(1, 1), groups=2, bias=False
                (bn): BatchNorm2d(144, eps=1e-05, momentum=0.03, affine=True, track_running_stats=True)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
        )
      )
      (head0): Module(
        (shared_layer_8): Module(
          (conv0): Module(
            (conv0): ConvReLU2d(
              10, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv1): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv2): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_8_obj): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 1, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_8_cls): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 3, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_8_box): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 4, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (shared_layer_16): Module(
          (conv0): Module(
            (conv0): ConvReLU2d(
              52, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv1): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv2): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_16_obj): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 1, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_16_cls): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 3, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_16_box): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 4, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (shared_layer_32): Module(
          (conv0): Module(
            (conv0): ConvReLU2d(
              144, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv1): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv2): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_obj): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 1, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_cls): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 3, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_box): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 4, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
      )
      (x_post_act_fake_quantizer): FixedFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=0, quant_max=255, dtype=torch.quint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32)
        (activation_post_process): PseudoObserver(min_val=0.0, max_val=1.0, pot=False)
      )
      (backbone0_backbone_0_0_conv_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_1_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_1_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_1_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_2_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_3_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_4_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_8_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_8_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_8_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_8_conv1_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_8_conv2_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_16_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_16_conv1_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_16_conv2_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_8_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_32_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_32_conv1_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_32_conv2_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_layer_8_obj_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_8_cls_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_8_box_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_layer_16_obj_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_16_cls_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_16_box_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_layer_32_obj_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_32_cls_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_32_box_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
    )
    (loss): YOLOXLoss(
      (l1_loss): L1Loss()
      (bcewithlog_loss): BCEWithLogitsLoss()
      (iou_loss): IOUloss()
    )
  )
)
2025-08-01 02:09:44.974 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch1
2025-08-01 02:09:44.998 | INFO     | yolox_microbt.core.trainer:before_epoch:200 - --->No mosaic aug for calibration model!
2025-08-01 02:09:49.715 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/600, iter: 20/129, gpu mem: 1201Mb, mem: 76.0Gb, iter_time: 0.235s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 3.101e-05, size: 480, ETA: 5:02:33
2025-08-01 02:09:53.145 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/600, iter: 40/129, gpu mem: 1201Mb, mem: 76.0Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.8, l1_loss: 0.0, conf_loss: 1.3, cls_loss: 0.6, lr: 6.202e-05, size: 448, ETA: 4:21:13
2025-08-01 02:09:56.254 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/600, iter: 60/129, gpu mem: 1201Mb, mem: 76.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 9.302e-05, size: 352, ETA: 4:00:24
2025-08-01 02:09:59.766 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/600, iter: 80/129, gpu mem: 1922Mb, mem: 76.0Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 1.5, cls_loss: 0.6, lr: 1.240e-04, size: 544, ETA: 3:56:30
2025-08-01 02:10:03.409 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.0Gb, iter_time: 0.180s, 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: 1.550e-04, size: 576, ETA: 3:55:38
2025-08-01 02:10:06.771 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.0Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 3.6, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 0.8, cls_loss: 0.6, lr: 1.860e-04, size: 352, ETA: 3:52:11
2025-08-01 02:10:08.403 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:10:15.196 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:10:16.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:10:16.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6255
2025-08-01 02:10:16.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5729
2025-08-01 02:10:16.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4409
2025-08-01 02:10:16.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5465
2025-08-01 02:10:16.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.626
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.546
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:10:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:10:16.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:10:16.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:10:17.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:10:18.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:10:19.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:10:19.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:10:20.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:10:21.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:10:21.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:10:22.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:10:23.264 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:10:23.264 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.30
2025-08-01 02:10:23.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.55
2025-08-01 02:10:23.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:10:23.272 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.86 ms, Average inference time: 8.20 ms

2025-08-01 02:10:23.274 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:10:23.355 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:10:23.436 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch2
2025-08-01 02:10:23.441 | INFO     | yolox_microbt.core.trainer:before_epoch:200 - --->No mosaic aug for calibration model!
2025-08-01 02:10:26.407 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.0, conf_loss: 1.2, cls_loss: 0.5, lr: 2.310e-04, size: 544, ETA: 3:46:16
2025-08-01 02:10:29.273 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.4, cls_loss: 0.7, lr: 2.620e-04, size: 320, ETA: 3:41:03
2025-08-01 02:10:32.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.5, l1_loss: 0.0, conf_loss: 1.2, cls_loss: 0.7, lr: 2.930e-04, size: 416, ETA: 3:39:23
2025-08-01 02:10:35.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 2.9, iou_loss: 1.4, l1_loss: 0.0, conf_loss: 1.0, cls_loss: 0.5, lr: 3.240e-04, size: 480, ETA: 3:36:40
2025-08-01 02:10:38.443 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.0, conf_loss: 1.6, cls_loss: 0.5, lr: 3.550e-04, size: 544, ETA: 3:34:05
2025-08-01 02:10:41.343 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.1Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 3.2, iou_loss: 1.6, l1_loss: 0.0, conf_loss: 1.1, cls_loss: 0.5, lr: 3.860e-04, size: 544, ETA: 3:31:39
2025-08-01 02:10:42.721 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:10:49.663 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:10:50.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:10:50.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5893
2025-08-01 02:10:50.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5638
2025-08-01 02:10:51.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3867
2025-08-01 02:10:51.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5133
2025-08-01 02:10:51.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:10:51.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:10:51.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-08-01 02:10:51.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-08-01 02:10:51.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:10:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:10:51.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:10:52.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:10:52.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:10:52.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:10:53.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:10:53.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:10:54.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:10:54.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:10:55.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:10:55.300 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-08-01 02:10:55.300 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-01 02:10:55.300 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:10:55.307 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.83 ms, Average inference time: 8.14 ms

2025-08-01 02:10:55.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:10:55.431 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:10:55.513 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch3
2025-08-01 02:10:55.573 | INFO     | yolox_microbt.core.trainer:before_epoch:204 - --->enable mosaic aug for quantization training!
2025-08-01 02:10:59.121 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 4.310e-04, size: 448, ETA: 3:32:02
2025-08-01 02:11:02.619 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.7, lr: 4.620e-04, size: 320, ETA: 3:32:43
2025-08-01 02:11:06.397 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.188s, 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: 4.930e-04, size: 544, ETA: 3:34:27
2025-08-01 02:11:09.862 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.169s, 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: 5.240e-04, size: 256, ETA: 3:34:35
2025-08-01 02:11:13.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.6, lr: 5.550e-04, size: 480, ETA: 3:35:18
2025-08-01 02:11:17.201 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.183s, 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: 5.860e-04, size: 352, ETA: 3:36:17
2025-08-01 02:11:19.045 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:11:25.808 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:11:28.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:11:30.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4517
2025-08-01 02:11:30.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4133
2025-08-01 02:11:30.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2442
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3698
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:11:30.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:11:30.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:11:30.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:11:30.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:11:30.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:11:30.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:11:30.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:11:33.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:11:35.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:11:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:11:39.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:11:41.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:11:44.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:11:46.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:11:48.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:11:50.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:11:50.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 02:11:50.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 02:11:50.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:11:50.762 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.90 ms, Average inference time: 8.37 ms

2025-08-01 02:11:50.762 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:11:50.836 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:11:50.917 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch4
2025-08-01 02:11:54.514 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 6.310e-04, size: 448, ETA: 3:37:32
2025-08-01 02:11:58.373 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.188s, 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: 6.620e-04, size: 288, ETA: 3:38:34
2025-08-01 02:12:02.352 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.196s, 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: 6.930e-04, size: 384, ETA: 3:39:57
2025-08-01 02:12:05.994 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 7.240e-04, size: 448, ETA: 3:40:25
2025-08-01 02:12:09.683 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.180s, 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: 7.550e-04, size: 256, ETA: 3:40:48
2025-08-01 02:12:13.503 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 7.860e-04, size: 544, ETA: 3:41:30
2025-08-01 02:12:15.277 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:12:22.075 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:12:23.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:12:24.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4788
2025-08-01 02:12:25.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3955
2025-08-01 02:12:25.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2211
2025-08-01 02:12:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3651
2025-08-01 02:12:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:12:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:12:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-01 02:12:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-01 02:12:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-08-01 02:12:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.365
2025-08-01 02:12:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:12:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:12:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:12:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:12:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:12:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:12:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:12:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:12:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:12:26.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:12:27.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:12:29.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:12:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:12:31.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:12:33.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:12:34.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:12:36.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:12:37.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:12:37.358 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 02:12:37.358 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 02:12:37.358 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:12:37.368 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.84 ms, Average inference time: 8.28 ms

2025-08-01 02:12:37.369 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:12:37.441 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:12:37.523 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch5
2025-08-01 02:12:41.168 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 8.310e-04, size: 288, ETA: 3:42:03
2025-08-01 02:12:44.927 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 8.620e-04, size: 576, ETA: 3:42:35
2025-08-01 02:12:48.845 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.193s, 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: 8.930e-04, size: 480, ETA: 3:43:24
2025-08-01 02:12:52.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 9.240e-04, size: 416, ETA: 3:44:05
2025-08-01 02:12:56.504 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.181s, 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: 9.550e-04, size: 576, ETA: 3:44:17
2025-08-01 02:13:00.329 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 9.860e-04, size: 352, ETA: 3:44:49
2025-08-01 02:13:01.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:13:08.872 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:13:11.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:13:13.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5300
2025-08-01 02:13:13.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4637
2025-08-01 02:13:13.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2929
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4289
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:13:13.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:13:13.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:13:13.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:13:13.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:13:13.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:13:13.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:13:13.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:13:13.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:13:15.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:13:18.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:13:20.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:13:22.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:13:24.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:13:26.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:13:29.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:13:31.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:13:33.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:13:33.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-01 02:13:33.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 02:13:33.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:13:33.425 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.88 ms, Average inference time: 8.30 ms

2025-08-01 02:13:33.426 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:13:33.506 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:13:33.590 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch6
2025-08-01 02:13:37.323 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.182s, 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: 1.031e-03, size: 576, ETA: 3:45:02
2025-08-01 02:13:41.097 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.186s, 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: 1.062e-03, size: 352, ETA: 3:45:21
2025-08-01 02:13:44.770 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.181s, 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: 1.093e-03, size: 512, ETA: 3:45:27
2025-08-01 02:13:48.448 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.124e-03, size: 320, ETA: 3:45:32
2025-08-01 02:13:52.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.155e-03, size: 544, ETA: 3:45:22
2025-08-01 02:13:55.772 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.2Gb, iter_time: 0.182s, 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: 1.186e-03, size: 416, ETA: 3:45:29
2025-08-01 02:13:57.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:14:04.460 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:14:09.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:14:12.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4244
2025-08-01 02:14:13.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3686
2025-08-01 02:14:13.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1528
2025-08-01 02:14:13.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3153
2025-08-01 02:14:13.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:14:13.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:14:13.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 02:14:13.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 02:14:13.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-08-01 02:14:13.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.315
2025-08-01 02:14:13.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:14:13.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:14:13.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:14:13.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:14:13.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:14:13.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:14:13.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:14:13.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:14:13.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:14:17.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:14:22.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:14:26.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:14:30.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:14:35.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:14:39.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:14:43.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:14:47.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:14:52.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:14:52.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 02:14:52.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 02:14:52.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:14:52.221 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.89 ms, Average inference time: 8.34 ms

2025-08-01 02:14:52.222 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:14:52.299 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:14:52.398 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch7
2025-08-01 02:14:56.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.231e-03, size: 512, ETA: 3:45:35
2025-08-01 02:14:59.956 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.190s, 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: 1.262e-03, size: 544, ETA: 3:45:56
2025-08-01 02:15:03.666 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.293e-03, size: 320, ETA: 3:46:03
2025-08-01 02:15:07.302 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.178s, 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: 1.324e-03, size: 416, ETA: 3:46:01
2025-08-01 02:15:10.910 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.179s, 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: 1.355e-03, size: 384, ETA: 3:46:02
2025-08-01 02:15:14.557 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.386e-03, size: 384, ETA: 3:46:05
2025-08-01 02:15:16.168 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:15:22.844 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:15:24.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:15:26.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4553
2025-08-01 02:15:26.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3593
2025-08-01 02:15:26.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2285
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3477
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.228
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.348
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:15:26.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:15:26.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:15:26.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:15:26.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:15:26.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:15:26.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:15:26.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:15:26.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:15:28.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:15:29.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:15:31.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:15:32.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:15:34.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:15:35.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:15:37.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:15:38.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:15:40.431 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:15:40.431 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 02:15:40.431 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 02:15:40.431 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:15:40.457 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.86 ms, Average inference time: 8.38 ms

2025-08-01 02:15:40.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:15:40.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:15:40.618 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch8
2025-08-01 02:15:44.109 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.170s, 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: 1.431e-03, size: 256, ETA: 3:45:47
2025-08-01 02:15:47.666 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.175s, 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: 1.462e-03, size: 512, ETA: 3:45:40
2025-08-01 02:15:51.504 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.493e-03, size: 480, ETA: 3:45:58
2025-08-01 02:15:55.373 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.189s, 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: 1.524e-03, size: 256, ETA: 3:46:13
2025-08-01 02:15:58.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.555e-03, size: 544, ETA: 3:46:01
2025-08-01 02:16:02.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 9.1, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 0.8, lr: 1.586e-03, size: 256, ETA: 3:46:01
2025-08-01 02:16:04.179 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:16:11.184 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:16:15.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:16:17.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3314
2025-08-01 02:16:17.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2888
2025-08-01 02:16:17.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1568
2025-08-01 02:16:17.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2590
2025-08-01 02:16:17.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:16:17.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:16:17.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-08-01 02:16:17.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.157
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.259
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:16:17.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:16:20.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:16:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:16:25.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:16:28.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:16:31.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:16:33.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:16:36.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:16:39.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:16:41.717 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:16:41.717 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 02:16:41.717 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 02:16:41.717 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:16:41.745 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.88 ms, Average inference time: 8.35 ms

2025-08-01 02:16:41.746 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:16:41.838 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:16:41.940 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch9
2025-08-01 02:16:45.374 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.170s, 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: 1.631e-03, size: 320, ETA: 3:45:38
2025-08-01 02:16:48.915 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.662e-03, size: 512, ETA: 3:45:31
2025-08-01 02:16:52.852 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.196s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.693e-03, size: 256, ETA: 3:45:53
2025-08-01 02:16:56.636 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.724e-03, size: 416, ETA: 3:46:01
2025-08-01 02:17:00.223 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.755e-03, size: 512, ETA: 3:45:52
2025-08-01 02:17:03.945 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.8, lr: 1.786e-03, size: 320, ETA: 3:45:55
2025-08-01 02:17:05.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:17:12.427 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:17:13.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:17:14.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2694
2025-08-01 02:17:14.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2877
2025-08-01 02:17:14.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1483
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2351
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.148
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.235
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:17:14.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:17:14.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:17:14.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:17:14.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:17:14.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:17:14.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:17:15.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:17:16.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:17:17.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:17:18.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:17:19.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:17:20.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:17:21.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:17:22.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:17:23.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:17:23.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 02:17:23.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 02:17:23.881 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:17:23.888 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.86 ms, Average inference time: 8.44 ms

2025-08-01 02:17:23.890 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:17:23.967 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:17:24.049 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch10
2025-08-01 02:17:27.589 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.173s, 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: 1.831e-03, size: 448, ETA: 3:45:47
2025-08-01 02:17:31.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.862e-03, size: 448, ETA: 3:45:48
2025-08-01 02:17:35.001 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.1, lr: 1.893e-03, size: 256, ETA: 3:45:52
2025-08-01 02:17:38.687 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.181s, 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: 1.924e-03, size: 480, ETA: 3:45:51
2025-08-01 02:17:42.470 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.7, lr: 1.955e-03, size: 544, ETA: 3:45:56
2025-08-01 02:17:46.199 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.986e-03, size: 480, ETA: 3:45:59
2025-08-01 02:17:47.963 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:17:54.991 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:17:57.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:17:58.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4409
2025-08-01 02:17:59.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4178
2025-08-01 02:17:59.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2092
2025-08-01 02:17:59.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3560
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:17:59.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:17:59.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:17:59.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:17:59.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:17:59.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:17:59.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:17:59.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:18:01.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:18:03.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:18:05.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:18:07.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:18:09.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:18:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:18:13.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:18:15.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:18:17.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:18:17.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 02:18:17.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 02:18:17.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:18:17.199 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.86 ms, Average inference time: 8.36 ms

2025-08-01 02:18:17.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:18:17.279 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:18:17.363 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch11
2025-08-01 02:18:21.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 2.000e-03, size: 544, ETA: 3:46:06
2025-08-01 02:18:24.834 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 2.000e-03, size: 512, ETA: 3:46:08
2025-08-01 02:18:28.578 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 2.000e-03, size: 512, ETA: 3:46:12
2025-08-01 02:18:32.451 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.190s, 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: 2.000e-03, size: 512, ETA: 3:46:22
2025-08-01 02:18:36.203 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 2.000e-03, size: 544, ETA: 3:46:21
2025-08-01 02:18:39.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, 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: 2.000e-03, size: 384, ETA: 3:46:23
2025-08-01 02:18:41.557 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:18:48.263 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:18:49.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:18:50.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3948
2025-08-01 02:18:50.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3402
2025-08-01 02:18:50.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1962
2025-08-01 02:18:50.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3104
2025-08-01 02:18:50.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:18:50.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:18:50.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-01 02:18:50.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-01 02:18:50.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.196
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.310
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:18:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:18:51.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:18:52.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:18:53.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:18:54.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:18:55.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:18:56.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:18:57.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:18:58.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:18:59.663 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:18:59.663 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 02:18:59.663 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 02:18:59.663 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:18:59.671 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.87 ms, Average inference time: 8.39 ms

2025-08-01 02:18:59.673 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:18:59.749 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:18:59.832 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch12
2025-08-01 02:19:03.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.183s, 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: 2.000e-03, size: 416, ETA: 3:46:22
2025-08-01 02:19:07.370 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, 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: 2.000e-03, size: 512, ETA: 3:46:24
2025-08-01 02:19:11.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.187s, 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: 2.000e-03, size: 480, ETA: 3:46:29
2025-08-01 02:19:14.994 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.187s, 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: 2.000e-03, size: 288, ETA: 3:46:34
2025-08-01 02:19:18.846 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 2.000e-03, size: 352, ETA: 3:46:41
2025-08-01 02:19:22.642 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.006s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 2.000e-03, size: 448, ETA: 3:46:43
2025-08-01 02:19:24.269 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:19:30.972 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:19:32.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:19:33.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4285
2025-08-01 02:19:33.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4128
2025-08-01 02:19:33.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2048
2025-08-01 02:19:33.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3487
2025-08-01 02:19:33.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:19:33.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:19:33.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-01 02:19:33.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 02:19:33.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.205
2025-08-01 02:19:33.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.349
2025-08-01 02:19:33.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:19:33.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:19:33.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:19:33.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:19:33.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:19:33.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:19:33.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:19:33.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:19:33.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:19:35.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:19:36.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:19:37.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:19:38.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:19:39.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:19:41.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:19:42.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:19:43.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:19:44.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:19:44.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:19:44.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 02:19:44.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:19:44.715 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.86 ms, Average inference time: 8.29 ms

2025-08-01 02:19:44.716 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:19:44.791 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:19:44.878 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch13
2025-08-01 02:19:48.463 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.178s, 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: 2.000e-03, size: 256, ETA: 3:46:37
2025-08-01 02:19:52.056 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.179s, 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: 2.000e-03, size: 512, ETA: 3:46:32
2025-08-01 02:19:55.635 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 2.000e-03, size: 416, ETA: 3:46:24
2025-08-01 02:19:59.445 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 2.000e-03, size: 544, ETA: 3:46:29
2025-08-01 02:20:03.224 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 2.000e-03, size: 384, ETA: 3:46:30
2025-08-01 02:20:06.919 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 2.000e-03, size: 416, ETA: 3:46:29
2025-08-01 02:20:08.509 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:20:15.236 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:20:17.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:20:18.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4960
2025-08-01 02:20:18.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4265
2025-08-01 02:20:18.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2658
2025-08-01 02:20:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3961
2025-08-01 02:20:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:20:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:20:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-01 02:20:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-08-01 02:20:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-08-01 02:20:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:20:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:20:20.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:20:21.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:20:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:20:24.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:20:26.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:20:27.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:20:29.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:20:30.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:20:32.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:20:32.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:20:32.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 02:20:32.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:20:32.117 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.88 ms, Average inference time: 8.33 ms

2025-08-01 02:20:32.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:20:32.201 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:20:32.286 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch14
2025-08-01 02:20:35.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 2.000e-03, size: 320, ETA: 3:46:09
2025-08-01 02:20:39.353 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.180s, 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: 2.000e-03, size: 384, ETA: 3:46:07
2025-08-01 02:20:43.182 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 2.000e-03, size: 384, ETA: 3:46:10
2025-08-01 02:20:46.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 2.000e-03, size: 384, ETA: 3:46:06
2025-08-01 02:20:50.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.179s, 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: 2.000e-03, size: 384, ETA: 3:46:02
2025-08-01 02:20:54.229 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 2.000e-03, size: 256, ETA: 3:46:02
2025-08-01 02:20:55.804 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:21:02.592 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:21:04.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:21:04.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4276
2025-08-01 02:21:05.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4017
2025-08-01 02:21:05.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1870
2025-08-01 02:21:05.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3388
2025-08-01 02:21:05.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.187
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.339
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:21:05.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:21:05.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:21:05.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:21:05.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:21:05.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:21:05.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:21:06.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:21:07.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:21:08.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:21:09.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:21:10.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:21:11.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:21:12.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:21:14.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:21:15.201 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:21:15.201 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:21:15.201 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 02:21:15.201 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:21:15.209 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.86 ms, Average inference time: 8.37 ms

2025-08-01 02:21:15.224 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:21:15.297 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:21:15.383 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch15
2025-08-01 02:21:18.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.9, lr: 2.000e-03, size: 256, ETA: 3:45:46
2025-08-01 02:21:22.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 2.000e-03, size: 512, ETA: 3:45:40
2025-08-01 02:21:26.134 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 2.000e-03, size: 448, ETA: 3:45:36
2025-08-01 02:21:29.851 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 2.000e-03, size: 448, ETA: 3:45:33
2025-08-01 02:21:33.718 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 2.000e-03, size: 512, ETA: 3:45:37
2025-08-01 02:21:37.490 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 2.000e-03, size: 416, ETA: 3:45:38
2025-08-01 02:21:39.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:21:45.904 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:21:48.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:21:51.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4808
2025-08-01 02:21:51.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4610
2025-08-01 02:21:51.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1983
2025-08-01 02:21:51.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3800
2025-08-01 02:21:51.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:21:51.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:21:51.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 02:21:51.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-08-01 02:21:51.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-08-01 02:21:51.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:21:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:21:54.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:21:56.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:21:59.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:22:01.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:22:04.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:22:07.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:22:09.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:22:12.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:22:14.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:22:14.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 02:22:14.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 02:22:14.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:22:14.921 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.85 ms, Average inference time: 8.18 ms

2025-08-01 02:22:14.922 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:22:14.997 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:22:15.083 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch16
2025-08-01 02:22:18.608 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.175s, 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: 2.000e-03, size: 352, ETA: 3:45:29
2025-08-01 02:22:22.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 2.000e-03, size: 352, ETA: 3:45:23
2025-08-01 02:22:25.964 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 2.000e-03, size: 352, ETA: 3:45:24
2025-08-01 02:22:29.796 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 2.000e-03, size: 288, ETA: 3:45:29
2025-08-01 02:22:33.831 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.196s, 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: 2.000e-03, size: 448, ETA: 3:45:38
2025-08-01 02:22:37.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 2.000e-03, size: 416, ETA: 3:45:36
2025-08-01 02:22:39.229 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:22:46.136 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:22:49.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:22:51.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3836
2025-08-01 02:22:51.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3617
2025-08-01 02:22:51.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1906
2025-08-01 02:22:51.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3120
2025-08-01 02:22:51.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:22:51.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.312
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:22:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:22:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:22:54.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:22:56.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:22:59.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:23:02.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:23:04.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:23:07.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:23:09.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:23:12.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:23:14.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:23:14.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:23:14.817 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 02:23:14.817 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:23:14.845 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.87 ms, Average inference time: 8.30 ms

2025-08-01 02:23:14.846 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:23:14.977 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:23:15.065 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch17
2025-08-01 02:23:18.607 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.174s, 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: 1.999e-03, size: 544, ETA: 3:45:28
2025-08-01 02:23:22.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.180s, 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: 1.999e-03, size: 320, ETA: 3:45:25
2025-08-01 02:23:25.934 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.999e-03, size: 320, ETA: 3:45:17
2025-08-01 02:23:29.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.999e-03, size: 288, ETA: 3:45:15
2025-08-01 02:23:33.432 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.999e-03, size: 448, ETA: 3:45:17
2025-08-01 02:23:37.100 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.181s, 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: 1.999e-03, size: 320, ETA: 3:45:14
2025-08-01 02:23:38.707 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:23:45.457 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:23:47.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:23:48.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4480
2025-08-01 02:23:48.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3748
2025-08-01 02:23:49.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2404
2025-08-01 02:23:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3544
2025-08-01 02:23:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:23:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:23:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-08-01 02:23:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-01 02:23:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-08-01 02:23:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.354
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:23:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:23:50.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:23:52.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:23:53.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:23:55.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:23:56.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:23:58.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:23:59.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:24:01.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:24:02.769 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:24:02.770 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:24:02.770 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 02:24:02.770 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:24:02.794 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.85 ms, Average inference time: 8.34 ms

2025-08-01 02:24:02.795 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:24:02.874 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:24:02.961 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch18
2025-08-01 02:24:06.689 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.999e-03, size: 480, ETA: 3:45:10
2025-08-01 02:24:10.344 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.999e-03, size: 320, ETA: 3:45:06
2025-08-01 02:24:14.000 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.999e-03, size: 256, ETA: 3:45:02
2025-08-01 02:24:17.627 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.999e-03, size: 384, ETA: 3:44:58
2025-08-01 02:24:21.470 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.999e-03, size: 480, ETA: 3:45:00
2025-08-01 02:24:25.108 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.999e-03, size: 384, ETA: 3:44:57
2025-08-01 02:24:26.715 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:24:33.427 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:24:34.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:24:35.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4796
2025-08-01 02:24:36.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3868
2025-08-01 02:24:36.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2502
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3722
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.250
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.372
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:24:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:24:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:24:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:24:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:24:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:24:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:24:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:24:37.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:24:38.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:24:39.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:24:40.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:24:41.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:24:43.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:24:44.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:24:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:24:46.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:24:46.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:24:46.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 02:24:46.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:24:46.540 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.88 ms, Average inference time: 8.41 ms

2025-08-01 02:24:46.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:24:46.617 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:24:46.701 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch19
2025-08-01 02:24:50.317 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.999e-03, size: 384, ETA: 3:44:48
2025-08-01 02:24:53.964 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.999e-03, size: 544, ETA: 3:44:43
2025-08-01 02:24:57.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.999e-03, size: 352, ETA: 3:44:44
2025-08-01 02:25:01.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.194s, 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: 1.999e-03, size: 320, ETA: 3:44:50
2025-08-01 02:25:05.470 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, 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: 512, ETA: 3:44:49
2025-08-01 02:25:09.122 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.999e-03, size: 256, ETA: 3:44:46
2025-08-01 02:25:10.731 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:25:17.654 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:25:19.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:25:19.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1801
2025-08-01 02:25:19.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1831
2025-08-01 02:25:20.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1147
2025-08-01 02:25:20.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1593
2025-08-01 02:25:20.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:25:20.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.180
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.183
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.115
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.159
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:25:20.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:25:20.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:25:20.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:25:21.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:25:21.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:25:22.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:25:23.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:25:24.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:25:25.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:25:26.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:25:27.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:25:28.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:25:28.573 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-08-01 02:25:28.573 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.16
2025-08-01 02:25:28.573 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:25:28.581 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.84 ms, Average inference time: 8.31 ms

2025-08-01 02:25:28.582 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:25:28.710 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:25:28.798 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch20
2025-08-01 02:25:32.316 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.999e-03, size: 288, ETA: 3:44:32
2025-08-01 02:25:35.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.999e-03, size: 320, ETA: 3:44:22
2025-08-01 02:25:39.524 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.999e-03, size: 576, ETA: 3:44:21
2025-08-01 02:25:43.311 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.999e-03, size: 416, ETA: 3:44:21
2025-08-01 02:25:47.018 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.999e-03, size: 512, ETA: 3:44:20
2025-08-01 02:25:50.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.999e-03, size: 544, ETA: 3:44:19
2025-08-01 02:25:52.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:25:59.262 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:26:01.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:26:02.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4980
2025-08-01 02:26:02.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4162
2025-08-01 02:26:02.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2617
2025-08-01 02:26:02.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3920
2025-08-01 02:26:02.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:26:02.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.262
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.392
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:26:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:26:02.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:26:02.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:26:04.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:26:05.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:26:07.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:26:08.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:26:10.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:26:11.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:26:13.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:26:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:26:16.475 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:26:16.475 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 02:26:16.475 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 02:26:16.475 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:26:16.488 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.59 ms, Average NMS time: 0.87 ms, Average inference time: 8.45 ms

2025-08-01 02:26:16.490 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:26:16.567 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:26:16.651 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch21
2025-08-01 02:26:20.135 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.999e-03, size: 320, ETA: 3:44:11
2025-08-01 02:26:23.765 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.180s, 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: 1.998e-03, size: 416, ETA: 3:44:08
2025-08-01 02:26:27.242 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.998e-03, size: 256, ETA: 3:43:59
2025-08-01 02:26:31.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.998e-03, size: 448, ETA: 3:43:59
2025-08-01 02:26:34.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.181s, 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: 1.998e-03, size: 352, ETA: 3:43:56
2025-08-01 02:26:38.630 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.191s, 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: 1.998e-03, size: 288, ETA: 3:43:59
2025-08-01 02:26:40.347 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:26:47.210 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:26:48.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:26:48.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2376
2025-08-01 02:26:48.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1497
2025-08-01 02:26:48.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1274
2025-08-01 02:26:48.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1715
2025-08-01 02:26:48.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:26:48.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:26:48.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-08-01 02:26:48.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.150
2025-08-01 02:26:48.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.127
2025-08-01 02:26:48.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.172
2025-08-01 02:26:48.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:26:48.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:26:48.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:26:48.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:26:48.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:26:48.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:26:48.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:26:48.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:26:48.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:26:49.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:26:50.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:26:51.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:26:51.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:26:52.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:26:53.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:26:54.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:26:54.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:26:55.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:26:55.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-08-01 02:26:55.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.17
2025-08-01 02:26:55.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:26:55.597 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.86 ms, Average inference time: 8.36 ms

2025-08-01 02:26:55.600 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:26:55.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:26:55.753 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch22
2025-08-01 02:26:59.451 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.998e-03, size: 448, ETA: 3:43:57
2025-08-01 02:27:03.157 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.998e-03, size: 480, ETA: 3:43:55
2025-08-01 02:27:06.995 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.188s, 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: 1.998e-03, size: 320, ETA: 3:43:55
2025-08-01 02:27:10.599 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.998e-03, size: 448, ETA: 3:43:51
2025-08-01 02:27:14.084 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.998e-03, size: 320, ETA: 3:43:43
2025-08-01 02:27:17.729 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.178s, 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: 1.998e-03, size: 288, ETA: 3:43:39
2025-08-01 02:27:19.455 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:27:26.187 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:27:27.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:27:28.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3377
2025-08-01 02:27:28.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2847
2025-08-01 02:27:28.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1764
2025-08-01 02:27:28.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2662
2025-08-01 02:27:28.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:27:28.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.176
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.266
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:27:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:27:28.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:27:28.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:27:28.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:27:28.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:27:29.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:27:31.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:27:32.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:27:33.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:27:34.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:27:35.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:27:36.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:27:37.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:27:38.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:27:38.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 02:27:38.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 02:27:38.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:27:38.514 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.87 ms, Average inference time: 8.20 ms

2025-08-01 02:27:38.515 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:27:38.589 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:27:38.674 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch23
2025-08-01 02:27:42.264 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.998e-03, size: 320, ETA: 3:43:34
2025-08-01 02:27:46.332 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.200s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.998e-03, size: 256, ETA: 3:43:41
2025-08-01 02:27:49.994 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.182s, 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: 1.998e-03, size: 512, ETA: 3:43:38
2025-08-01 02:27:53.686 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.998e-03, size: 384, ETA: 3:43:34
2025-08-01 02:27:57.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.187s, 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: 1.998e-03, size: 544, ETA: 3:43:34
2025-08-01 02:28:01.233 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.006s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.998e-03, size: 384, ETA: 3:43:33
2025-08-01 02:28:03.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:28:09.782 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:28:12.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:28:13.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4899
2025-08-01 02:28:14.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4143
2025-08-01 02:28:14.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2569
2025-08-01 02:28:14.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3870
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:28:14.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:28:14.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:28:14.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:28:14.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:28:14.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:28:15.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:28:17.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:28:19.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:28:21.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:28:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:28:26.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:28:28.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:28:30.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:28:32.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:28:32.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 02:28:32.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 02:28:32.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:28:32.614 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.64 ms, Average NMS time: 0.87 ms, Average inference time: 8.51 ms

2025-08-01 02:28:32.616 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:28:32.699 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:28:32.785 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch24
2025-08-01 02:28:36.525 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.998e-03, size: 352, ETA: 3:43:32
2025-08-01 02:28:40.432 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.193s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 1.997e-03, size: 416, ETA: 3:43:35
2025-08-01 02:28:43.992 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.997e-03, size: 352, ETA: 3:43:29
2025-08-01 02:28:47.762 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.185s, 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: 1.997e-03, size: 352, ETA: 3:43:28
2025-08-01 02:28:51.474 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.997e-03, size: 480, ETA: 3:43:26
2025-08-01 02:28:55.324 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.3Gb, iter_time: 0.187s, data_time: 0.006s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.997e-03, size: 512, ETA: 3:43:26
2025-08-01 02:28:56.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:29:03.752 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:29:11.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:29:15.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3874
2025-08-01 02:29:16.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3789
2025-08-01 02:29:16.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2268
2025-08-01 02:29:16.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3311
2025-08-01 02:29:16.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:29:16.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:29:16.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 02:29:16.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-01 02:29:16.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-08-01 02:29:16.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.331
2025-08-01 02:29:16.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:29:16.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:29:16.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:29:16.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:29:16.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:29:16.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:29:16.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:29:16.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:29:16.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:29:21.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:29:26.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:29:32.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:29:37.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:29:42.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:29:47.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:29:52.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:29:57.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:30:02.098 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:30:02.099 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:30:02.099 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 02:30:02.099 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:30:02.128 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 02:30:02.129 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:30:02.211 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:30:02.298 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch25
2025-08-01 02:30:05.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.997e-03, size: 576, ETA: 3:43:19
2025-08-01 02:30:09.574 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.997e-03, size: 384, ETA: 3:43:16
2025-08-01 02:30:13.227 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.176s, 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: 1.997e-03, size: 320, ETA: 3:43:10
2025-08-01 02:30:16.892 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.997e-03, size: 256, ETA: 3:43:06
2025-08-01 02:30:20.609 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.997e-03, size: 512, ETA: 3:43:04
2025-08-01 02:30:24.338 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.997e-03, size: 256, ETA: 3:43:01
2025-08-01 02:30:25.918 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:30:32.791 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:30:33.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:30:34.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4143
2025-08-01 02:30:34.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3334
2025-08-01 02:30:34.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1992
2025-08-01 02:30:34.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3156
2025-08-01 02:30:34.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.199
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.316
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:30:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:30:34.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:30:34.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:30:34.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:30:34.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:30:35.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:30:36.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:30:37.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:30:38.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:30:39.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:30:40.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:30:41.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:30:42.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:30:43.481 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:30:43.481 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:30:43.481 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 02:30:43.481 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:30:43.491 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.88 ms, Average inference time: 8.36 ms

2025-08-01 02:30:43.493 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:30:43.574 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:30:43.756 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch26
2025-08-01 02:30:47.585 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.997e-03, size: 256, ETA: 3:42:57
2025-08-01 02:30:51.491 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 1.997e-03, size: 256, ETA: 3:42:59
2025-08-01 02:30:55.064 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.997e-03, size: 256, ETA: 3:42:54
2025-08-01 02:30:58.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.176s, 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: 1.997e-03, size: 384, ETA: 3:42:48
2025-08-01 02:31:02.300 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.996e-03, size: 416, ETA: 3:42:44
2025-08-01 02:31:06.166 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.190s, data_time: 0.003s, total_loss: 6.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.996e-03, size: 288, ETA: 3:42:45
2025-08-01 02:31:07.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:31:14.719 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:31:15.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:31:16.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3593
2025-08-01 02:31:16.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3154
2025-08-01 02:31:16.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1890
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2879
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.189
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.288
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:31:16.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:31:16.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:31:16.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:31:16.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:31:16.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:31:16.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:31:16.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:31:16.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:31:17.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:31:18.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:31:19.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:31:19.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:31:20.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:31:21.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:31:22.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:31:23.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:31:23.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:31:23.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 02:31:23.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 02:31:23.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:31:23.983 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.86 ms, Average inference time: 8.35 ms

2025-08-01 02:31:23.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:31:24.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:31:24.173 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch27
2025-08-01 02:31:27.955 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.996e-03, size: 544, ETA: 3:42:44
2025-08-01 02:31:31.517 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.175s, 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: 1.996e-03, size: 320, ETA: 3:42:38
2025-08-01 02:31:35.320 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.996e-03, size: 384, ETA: 3:42:38
2025-08-01 02:31:38.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.996e-03, size: 480, ETA: 3:42:33
2025-08-01 02:31:42.750 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.186s, 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: 1.996e-03, size: 544, ETA: 3:42:32
2025-08-01 02:31:46.562 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.996e-03, size: 544, ETA: 3:42:31
2025-08-01 02:31:48.260 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:31:55.175 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:31:58.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:32:00.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4371
2025-08-01 02:32:01.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4027
2025-08-01 02:32:01.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2216
2025-08-01 02:32:01.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3538
2025-08-01 02:32:01.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:32:01.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:32:01.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 02:32:01.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-01 02:32:01.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.222
2025-08-01 02:32:01.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.354
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:32:01.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:32:04.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:32:06.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:32:09.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:32:12.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:32:14.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:32:17.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:32:19.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:32:22.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:32:24.729 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:32:24.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 02:32:24.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 02:32:24.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:32:24.755 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.87 ms, Average inference time: 8.45 ms

2025-08-01 02:32:24.759 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:32:24.842 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:32:24.927 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch28
2025-08-01 02:32:28.544 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.178s, 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: 1.996e-03, size: 512, ETA: 3:42:26
2025-08-01 02:32:32.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.172s, 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: 1.996e-03, size: 352, ETA: 3:42:19
2025-08-01 02:32:36.160 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.199s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.996e-03, size: 384, ETA: 3:42:23
2025-08-01 02:32:39.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.177s, 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: 1.996e-03, size: 480, ETA: 3:42:17
2025-08-01 02:32:43.652 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.188s, 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: 1.996e-03, size: 352, ETA: 3:42:17
2025-08-01 02:32:47.405 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.995e-03, size: 544, ETA: 3:42:15
2025-08-01 02:32:49.142 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:32:55.826 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:32:58.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:33:00.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5431
2025-08-01 02:33:00.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4700
2025-08-01 02:33:00.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2865
2025-08-01 02:33:00.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4332
2025-08-01 02:33:00.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:33:00.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:33:00.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-01 02:33:00.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-08-01 02:33:00.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:33:00.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:33:00.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:33:03.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:33:05.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:33:07.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:33:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:33:12.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:33:15.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:33:17.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:33:19.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:33:22.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:33:22.011 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-01 02:33:22.011 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 02:33:22.011 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:33:22.036 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.87 ms, Average inference time: 8.27 ms

2025-08-01 02:33:22.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:33:22.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:33:22.250 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch29
2025-08-01 02:33:25.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.995e-03, size: 544, ETA: 3:42:06
2025-08-01 02:33:29.510 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.185s, 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: 1.995e-03, size: 448, ETA: 3:42:05
2025-08-01 02:33:33.267 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.182s, 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: 1.995e-03, size: 512, ETA: 3:42:02
2025-08-01 02:33:37.031 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.995e-03, size: 544, ETA: 3:42:00
2025-08-01 02:33:40.772 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.2, lr: 1.995e-03, size: 256, ETA: 3:41:58
2025-08-01 02:33:44.406 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.995e-03, size: 416, ETA: 3:41:52
2025-08-01 02:33:45.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:33:52.862 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:33:54.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:33:55.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4492
2025-08-01 02:33:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3668
2025-08-01 02:33:56.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2160
2025-08-01 02:33:56.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3440
2025-08-01 02:33:56.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:33:56.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:33:56.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:33:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:33:56.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:33:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:33:59.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:34:00.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:34:02.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:34:03.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:34:04.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:34:06.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:34:07.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:34:09.369 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:34:09.369 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 02:34:09.369 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 02:34:09.369 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:34:09.393 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.87 ms, Average inference time: 8.22 ms

2025-08-01 02:34:09.394 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:34:09.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:34:09.562 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch30
2025-08-01 02:34:13.221 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.995e-03, size: 448, ETA: 3:41:45
2025-08-01 02:34:16.990 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.1, lr: 1.995e-03, size: 256, ETA: 3:41:44
2025-08-01 02:34:20.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.995e-03, size: 480, ETA: 3:41:40
2025-08-01 02:34:24.441 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.995e-03, size: 448, ETA: 3:41:37
2025-08-01 02:34:28.151 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.9, lr: 1.994e-03, size: 544, ETA: 3:41:34
2025-08-01 02:34:32.060 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.193s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.994e-03, size: 576, ETA: 3:41:35
2025-08-01 02:34:33.696 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:34:40.458 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:34:41.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:34:42.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3697
2025-08-01 02:34:43.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2717
2025-08-01 02:34:43.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1243
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2552
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.124
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.255
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:34:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:34:43.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:34:43.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:34:43.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:34:43.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:34:43.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:34:43.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:34:43.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:34:44.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:34:45.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:34:47.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:34:48.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:34:49.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:34:50.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:34:52.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:34:53.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:34:55.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:34:55.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 02:34:55.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 02:34:55.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:34:55.062 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.86 ms, Average inference time: 8.22 ms

2025-08-01 02:34:55.064 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:34:55.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:34:55.246 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch31
2025-08-01 02:34:58.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.172s, 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: 1.994e-03, size: 256, ETA: 3:41:26
2025-08-01 02:35:02.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.994e-03, size: 320, ETA: 3:41:21
2025-08-01 02:35:05.902 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.172s, 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: 1.994e-03, size: 288, ETA: 3:41:14
2025-08-01 02:35:09.519 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.994e-03, size: 352, ETA: 3:41:09
2025-08-01 02:35:13.407 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.193s, 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: 1.994e-03, size: 512, ETA: 3:41:10
2025-08-01 02:35:17.063 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.994e-03, size: 352, ETA: 3:41:07
2025-08-01 02:35:18.715 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:35:25.475 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:35:27.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:35:28.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3900
2025-08-01 02:35:28.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3050
2025-08-01 02:35:28.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1931
2025-08-01 02:35:28.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2960
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.296
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:35:28.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:35:28.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:35:28.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:35:28.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:35:28.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:35:28.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:35:28.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:35:29.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:35:31.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:35:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:35:33.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:35:35.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:35:36.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:35:37.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:35:39.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:35:40.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:35:40.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 02:35:40.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 02:35:40.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:35:40.407 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.84 ms, Average inference time: 8.29 ms

2025-08-01 02:35:40.409 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:35:40.527 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:35:40.630 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch32
2025-08-01 02:35:44.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.994e-03, size: 352, ETA: 3:41:01
2025-08-01 02:35:48.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.182s, 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: 1.994e-03, size: 512, ETA: 3:40:57
2025-08-01 02:35:51.555 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.993e-03, size: 256, ETA: 3:40:51
2025-08-01 02:35:55.334 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.993e-03, size: 448, ETA: 3:40:50
2025-08-01 02:35:59.122 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.187s, 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: 1.993e-03, size: 512, ETA: 3:40:48
2025-08-01 02:36:03.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.196s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.993e-03, size: 480, ETA: 3:40:50
2025-08-01 02:36:04.814 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:36:11.540 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:36:15.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:36:18.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5014
2025-08-01 02:36:18.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3890
2025-08-01 02:36:18.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2178
2025-08-01 02:36:18.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3694
2025-08-01 02:36:18.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:36:18.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:36:18.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-01 02:36:18.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.218
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:36:18.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:36:21.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:36:25.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:36:28.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:36:31.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:36:34.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:36:38.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:36:41.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:36:44.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:36:47.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:36:47.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 02:36:47.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 02:36:47.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:36:47.833 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.86 ms, Average inference time: 8.31 ms

2025-08-01 02:36:47.834 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:36:47.907 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:36:48.107 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch33
2025-08-01 02:36:51.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.993e-03, size: 320, ETA: 3:40:44
2025-08-01 02:36:55.293 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.993e-03, size: 288, ETA: 3:40:39
2025-08-01 02:36:58.919 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.178s, 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: 1.993e-03, size: 512, ETA: 3:40:34
2025-08-01 02:37:02.593 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.993e-03, size: 448, ETA: 3:40:30
2025-08-01 02:37:06.450 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.993e-03, size: 576, ETA: 3:40:29
2025-08-01 02:37:10.270 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.186s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.993e-03, size: 288, ETA: 3:40:27
2025-08-01 02:37:11.923 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:37:18.668 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:37:20.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:37:22.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4575
2025-08-01 02:37:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3875
2025-08-01 02:37:22.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2583
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3678
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.368
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:37:22.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:37:22.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:37:22.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:37:22.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:37:22.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:37:22.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:37:22.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:37:22.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:37:24.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:37:25.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:37:27.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:37:29.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:37:30.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:37:32.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:37:34.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:37:35.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:37:37.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:37:37.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 02:37:37.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 02:37:37.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:37:37.383 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.86 ms, Average inference time: 8.34 ms

2025-08-01 02:37:37.384 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:37:37.462 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:37:37.548 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch34
2025-08-01 02:37:41.166 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.992e-03, size: 576, ETA: 3:40:21
2025-08-01 02:37:45.097 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.194s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.992e-03, size: 544, ETA: 3:40:22
2025-08-01 02:37:48.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.2, lr: 1.992e-03, size: 256, ETA: 3:40:22
2025-08-01 02:37:52.626 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 1.992e-03, size: 352, ETA: 3:40:18
2025-08-01 02:37:56.472 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.992e-03, size: 352, ETA: 3:40:17
2025-08-01 02:38:00.349 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.191s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.992e-03, size: 256, ETA: 3:40:16
2025-08-01 02:38:02.009 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:38:08.959 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:38:10.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:38:11.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4858
2025-08-01 02:38:12.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4244
2025-08-01 02:38:12.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2755
2025-08-01 02:38:12.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3953
2025-08-01 02:38:12.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:38:12.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:38:12.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-01 02:38:12.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 02:38:12.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-08-01 02:38:12.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-08-01 02:38:12.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:38:12.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:38:12.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:38:12.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:38:12.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:38:12.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:38:12.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:38:12.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:38:12.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:38:13.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:38:15.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:38:16.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:38:17.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:38:19.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:38:21.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:38:22.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:38:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:38:25.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:38:25.407 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 02:38:25.407 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 02:38:25.407 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:38:25.416 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.88 ms, Average inference time: 8.38 ms

2025-08-01 02:38:25.418 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:38:25.490 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:38:25.574 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch35
2025-08-01 02:38:29.245 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.992e-03, size: 480, ETA: 3:40:12
2025-08-01 02:38:33.112 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.992e-03, size: 288, ETA: 3:40:12
2025-08-01 02:38:36.799 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.992e-03, size: 352, ETA: 3:40:08
2025-08-01 02:38:40.394 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.991e-03, size: 480, ETA: 3:40:03
2025-08-01 02:38:44.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.179s, 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: 1.991e-03, size: 384, ETA: 3:39:59
2025-08-01 02:38:47.778 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.991e-03, size: 320, ETA: 3:39:54
2025-08-01 02:38:49.493 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:38:56.373 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:38:57.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:38:57.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2452
2025-08-01 02:38:57.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2106
2025-08-01 02:38:57.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1255
2025-08-01 02:38:57.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1938
2025-08-01 02:38:57.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:38:57.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:38:57.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.245
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.126
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.194
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:38:57.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:38:57.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:38:58.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:38:58.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:38:59.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:38:59.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:39:00.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:39:00.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:39:01.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:39:01.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:39:02.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:39:02.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 02:39:02.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.19
2025-08-01 02:39:02.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:39:02.466 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.86 ms, Average inference time: 8.41 ms

2025-08-01 02:39:02.467 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:39:02.547 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:39:02.628 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch36
2025-08-01 02:39:06.210 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.176s, 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: 1.991e-03, size: 256, ETA: 3:39:48
2025-08-01 02:39:09.621 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.2, lr: 1.991e-03, size: 384, ETA: 3:39:40
2025-08-01 02:39:13.373 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.187s, 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: 1.991e-03, size: 288, ETA: 3:39:39
2025-08-01 02:39:16.816 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.991e-03, size: 320, ETA: 3:39:31
2025-08-01 02:39:20.562 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.991e-03, size: 384, ETA: 3:39:29
2025-08-01 02:39:24.255 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.990e-03, size: 256, ETA: 3:39:26
2025-08-01 02:39:25.810 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:39:32.613 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:39:35.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:39:37.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4035
2025-08-01 02:39:37.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3455
2025-08-01 02:39:38.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1688
2025-08-01 02:39:38.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3060
2025-08-01 02:39:38.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:39:38.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:39:38.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-01 02:39:38.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.346
2025-08-01 02:39:38.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-08-01 02:39:38.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.306
2025-08-01 02:39:38.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:39:38.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:39:38.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:39:38.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:39:38.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:39:38.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:39:38.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:39:38.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:39:38.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:39:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:39:43.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:39:45.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:39:47.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:39:50.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:39:52.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:39:55.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:39:57.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:40:00.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:40:00.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 02:40:00.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 02:40:00.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:40:00.317 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.86 ms, Average inference time: 8.41 ms

2025-08-01 02:40:00.320 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:40:00.449 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:40:00.537 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch37
2025-08-01 02:40:04.354 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.990e-03, size: 480, ETA: 3:39:21
2025-08-01 02:40:08.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.990e-03, size: 576, ETA: 3:39:18
2025-08-01 02:40:12.019 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.990e-03, size: 576, ETA: 3:39:18
2025-08-01 02:40:15.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.990e-03, size: 320, ETA: 3:39:15
2025-08-01 02:40:19.735 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.197s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.990e-03, size: 288, ETA: 3:39:16
2025-08-01 02:40:23.412 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.990e-03, size: 480, ETA: 3:39:13
2025-08-01 02:40:25.087 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:40:31.915 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:40:34.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:40:36.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4458
2025-08-01 02:40:36.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3690
2025-08-01 02:40:36.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2408
2025-08-01 02:40:36.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3518
2025-08-01 02:40:36.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:40:36.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:40:36.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-08-01 02:40:36.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.241
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:40:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:40:36.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:40:38.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:40:40.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:40:42.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:40:44.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:40:46.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:40:48.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:40:50.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:40:52.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:40:54.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:40:54.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 02:40:54.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 02:40:54.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:40:54.846 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.86 ms, Average inference time: 8.44 ms

2025-08-01 02:40:54.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:40:54.927 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:40:55.010 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch38
2025-08-01 02:40:58.533 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.990e-03, size: 416, ETA: 3:39:06
2025-08-01 02:41:02.315 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.989e-03, size: 416, ETA: 3:39:02
2025-08-01 02:41:06.082 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.989e-03, size: 576, ETA: 3:38:59
2025-08-01 02:41:09.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 1.989e-03, size: 544, ETA: 3:38:58
2025-08-01 02:41:13.619 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.2, lr: 1.989e-03, size: 576, ETA: 3:38:55
2025-08-01 02:41:17.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.198s, 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: 1.989e-03, size: 416, ETA: 3:38:56
2025-08-01 02:41:19.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:41:26.165 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:41:27.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:41:28.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4586
2025-08-01 02:41:28.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3845
2025-08-01 02:41:28.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2058
2025-08-01 02:41:28.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3497
2025-08-01 02:41:28.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:41:28.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.206
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.350
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:41:28.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:41:28.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:41:28.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:41:30.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:41:31.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:41:32.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:41:33.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:41:34.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:41:35.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:41:37.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:41:38.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:41:39.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:41:39.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:41:39.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 02:41:39.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:41:39.379 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.85 ms, Average inference time: 8.32 ms

2025-08-01 02:41:39.381 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:41:39.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:41:39.547 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch39
2025-08-01 02:41:43.369 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.989e-03, size: 512, ETA: 3:38:51
2025-08-01 02:41:47.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.989e-03, size: 352, ETA: 3:38:48
2025-08-01 02:41:50.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.989e-03, size: 544, ETA: 3:38:45
2025-08-01 02:41:54.775 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.195s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.988e-03, size: 480, ETA: 3:38:45
2025-08-01 02:41:58.524 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.988e-03, size: 320, ETA: 3:38:41
2025-08-01 02:42:02.212 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.178s, 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: 1.988e-03, size: 576, ETA: 3:38:37
2025-08-01 02:42:03.990 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:42:10.711 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:42:14.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:42:16.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4888
2025-08-01 02:42:16.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4342
2025-08-01 02:42:16.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1856
2025-08-01 02:42:16.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3695
2025-08-01 02:42:16.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:42:16.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:42:16.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-08-01 02:42:16.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-08-01 02:42:16.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:42:16.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:42:16.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:42:19.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:42:22.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:42:25.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:42:28.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:42:30.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:42:33.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:42:36.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:42:39.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:42:41.930 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:42:41.930 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:42:41.931 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 02:42:41.931 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:42:41.958 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.88 ms, Average inference time: 8.42 ms

2025-08-01 02:42:41.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:42:42.043 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:42:42.129 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch40
2025-08-01 02:42:45.733 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.178s, 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: 1.988e-03, size: 512, ETA: 3:38:33
2025-08-01 02:42:49.430 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.988e-03, size: 384, ETA: 3:38:30
2025-08-01 02:42:53.187 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.988e-03, size: 480, ETA: 3:38:27
2025-08-01 02:42:56.925 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.988e-03, size: 576, ETA: 3:38:24
2025-08-01 02:43:00.752 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.188s, 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: 1.987e-03, size: 448, ETA: 3:38:22
2025-08-01 02:43:04.545 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.188s, 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: 1.987e-03, size: 320, ETA: 3:38:20
2025-08-01 02:43:06.170 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:43:12.757 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:43:13.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:43:14.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2783
2025-08-01 02:43:14.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3345
2025-08-01 02:43:14.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1529
2025-08-01 02:43:14.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2552
2025-08-01 02:43:14.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.255
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:43:14.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:43:14.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:43:14.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:43:14.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:43:15.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:43:16.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:43:16.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:43:17.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:43:18.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:43:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:43:19.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:43:20.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:43:21.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:43:21.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 02:43:21.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 02:43:21.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:43:21.339 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.86 ms, Average inference time: 8.29 ms

2025-08-01 02:43:21.340 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:43:21.420 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:43:21.504 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch41
2025-08-01 02:43:25.067 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.176s, 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: 1.987e-03, size: 480, ETA: 3:38:13
2025-08-01 02:43:28.887 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, 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: 1.987e-03, size: 576, ETA: 3:38:10
2025-08-01 02:43:32.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.188s, 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: 1.987e-03, size: 544, ETA: 3:38:09
2025-08-01 02:43:36.472 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.987e-03, size: 480, ETA: 3:38:05
2025-08-01 02:43:40.347 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.192s, data_time: 0.006s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.987e-03, size: 352, ETA: 3:38:05
2025-08-01 02:43:44.001 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.986e-03, size: 320, ETA: 3:38:01
2025-08-01 02:43:45.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:43:52.292 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:43:54.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:43:55.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4016
2025-08-01 02:43:55.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3156
2025-08-01 02:43:55.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1746
2025-08-01 02:43:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2973
2025-08-01 02:43:55.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:43:55.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:43:55.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 02:43:55.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-01 02:43:55.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.175
2025-08-01 02:43:55.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.297
2025-08-01 02:43:55.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:43:55.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:43:55.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:43:55.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:43:55.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:43:55.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:43:55.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:43:55.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:43:55.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:43:57.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:43:59.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:44:00.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:44:02.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:44:03.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:44:05.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:44:06.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:44:08.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:44:09.870 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:44:09.870 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 02:44:09.871 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 02:44:09.871 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:44:09.895 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.86 ms, Average inference time: 8.33 ms

2025-08-01 02:44:09.903 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:44:09.987 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:44:10.073 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch42
2025-08-01 02:44:13.692 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, 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: 1.986e-03, size: 480, ETA: 3:37:53
2025-08-01 02:44:17.408 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.986e-03, size: 512, ETA: 3:37:50
2025-08-01 02:44:20.997 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.986e-03, size: 384, ETA: 3:37:45
2025-08-01 02:44:24.805 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.184s, 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: 1.986e-03, size: 288, ETA: 3:37:43
2025-08-01 02:44:28.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.986e-03, size: 256, ETA: 3:37:39
2025-08-01 02:44:32.184 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.3, lr: 1.986e-03, size: 288, ETA: 3:37:36
2025-08-01 02:44:33.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:44:40.598 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:44:42.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:44:43.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4032
2025-08-01 02:44:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3524
2025-08-01 02:44:43.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2254
2025-08-01 02:44:43.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3270
2025-08-01 02:44:43.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:44:43.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:44:43.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.225
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.327
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:44:43.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:44:44.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:44:45.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:44:46.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:44:48.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:44:49.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:44:50.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:44:51.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:44:53.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:44:54.274 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:44:54.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 02:44:54.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 02:44:54.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:44:54.283 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.26 ms, Average NMS time: 0.85 ms, Average inference time: 8.11 ms

2025-08-01 02:44:54.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:44:54.359 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:44:54.444 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch43
2025-08-01 02:44:58.251 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.188s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.985e-03, size: 576, ETA: 3:37:33
2025-08-01 02:45:02.236 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.197s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.985e-03, size: 576, ETA: 3:37:33
2025-08-01 02:45:06.033 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.985e-03, size: 384, ETA: 3:37:31
2025-08-01 02:45:09.709 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.985e-03, size: 288, ETA: 3:37:28
2025-08-01 02:45:13.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.187s, data_time: 0.006s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.985e-03, size: 416, ETA: 3:37:25
2025-08-01 02:45:17.253 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.985e-03, size: 544, ETA: 3:37:23
2025-08-01 02:45:18.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:45:25.800 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:45:29.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:45:32.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4739
2025-08-01 02:45:32.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3982
2025-08-01 02:45:32.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2873
2025-08-01 02:45:32.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3865
2025-08-01 02:45:32.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:45:32.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:45:32.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.386
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:45:32.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:45:35.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:45:38.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:45:41.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:45:44.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:45:47.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:45:50.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:45:53.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:45:57.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:46:00.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:46:00.077 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 02:46:00.077 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 02:46:00.077 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:46:00.104 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.88 ms, Average inference time: 8.27 ms

2025-08-01 02:46:00.105 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:46:00.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:46:00.324 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch44
2025-08-01 02:46:04.177 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.190s, 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: 1.984e-03, size: 544, ETA: 3:37:20
2025-08-01 02:46:08.070 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.984e-03, size: 480, ETA: 3:37:19
2025-08-01 02:46:11.807 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, 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: 1.984e-03, size: 480, ETA: 3:37:16
2025-08-01 02:46:15.574 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.984e-03, size: 352, ETA: 3:37:12
2025-08-01 02:46:19.213 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.984e-03, size: 416, ETA: 3:37:08
2025-08-01 02:46:22.934 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.2, lr: 1.984e-03, size: 448, ETA: 3:37:05
2025-08-01 02:46:24.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:46:31.377 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:46:35.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:46:38.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4229
2025-08-01 02:46:39.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3610
2025-08-01 02:46:39.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2421
2025-08-01 02:46:39.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3420
2025-08-01 02:46:39.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:46:39.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.342
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:46:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:46:39.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:46:39.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:46:39.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:46:42.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:46:46.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:46:49.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:46:53.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:46:56.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:47:00.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:47:03.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:47:07.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:47:11.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:47:11.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:47:11.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 02:47:11.043 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:47:11.067 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.88 ms, Average inference time: 8.40 ms

2025-08-01 02:47:11.069 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:47:11.147 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:47:11.231 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch45
2025-08-01 02:47:14.992 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.984e-03, size: 320, ETA: 3:36:59
2025-08-01 02:47:18.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.983e-03, size: 512, ETA: 3:36:54
2025-08-01 02:47:22.407 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.983e-03, size: 512, ETA: 3:36:52
2025-08-01 02:47:26.272 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.983e-03, size: 576, ETA: 3:36:51
2025-08-01 02:47:30.298 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.199s, 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: 1.983e-03, size: 288, ETA: 3:36:51
2025-08-01 02:47:34.084 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.983e-03, size: 288, ETA: 3:36:49
2025-08-01 02:47:35.697 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:47:42.508 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:47:43.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:47:44.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2384
2025-08-01 02:47:44.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2770
2025-08-01 02:47:44.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1256
2025-08-01 02:47:44.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2136
2025-08-01 02:47:44.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:47:44.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:47:44.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-08-01 02:47:44.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.277
2025-08-01 02:47:44.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.126
2025-08-01 02:47:44.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.214
2025-08-01 02:47:44.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:47:44.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:47:44.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:47:44.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:47:44.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:47:44.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:47:44.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:47:44.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:47:44.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:47:45.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:47:46.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:47:46.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:47:47.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:47:48.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:47:49.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:47:50.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:47:50.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:47:51.800 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:47:51.800 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-08-01 02:47:51.800 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.21
2025-08-01 02:47:51.801 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:47:51.807 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.30 ms, Average NMS time: 0.84 ms, Average inference time: 8.15 ms

2025-08-01 02:47:51.814 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:47:51.889 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:47:51.971 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch46
2025-08-01 02:47:55.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.983e-03, size: 320, ETA: 3:36:43
2025-08-01 02:47:59.286 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.179s, 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: 1.982e-03, size: 512, ETA: 3:36:38
2025-08-01 02:48:03.063 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.982e-03, size: 352, ETA: 3:36:36
2025-08-01 02:48:06.897 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.4Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.982e-03, size: 512, ETA: 3:36:34
2025-08-01 02:48:10.628 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.982e-03, size: 480, ETA: 3:36:31
2025-08-01 02:48:14.576 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.982e-03, size: 384, ETA: 3:36:30
2025-08-01 02:48:16.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:48:23.023 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:48:24.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:48:24.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2554
2025-08-01 02:48:24.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2179
2025-08-01 02:48:24.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1270
2025-08-01 02:48:24.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2001
2025-08-01 02:48:24.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:48:24.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.255
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.218
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.127
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.200
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:48:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:48:24.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:48:24.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:48:24.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:48:24.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:48:25.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:48:26.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:48:27.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:48:28.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:48:29.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:48:29.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:48:30.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:48:31.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:48:32.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:48:32.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 02:48:32.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.20
2025-08-01 02:48:32.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:48:32.175 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 02:48:32.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:48:32.289 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:48:32.444 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch47
2025-08-01 02:48:36.159 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.982e-03, size: 320, ETA: 3:36:25
2025-08-01 02:48:39.802 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.981e-03, size: 288, ETA: 3:36:21
2025-08-01 02:48:43.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.981e-03, size: 352, ETA: 3:36:17
2025-08-01 02:48:47.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.179s, 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: 1.981e-03, size: 416, ETA: 3:36:12
2025-08-01 02:48:50.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.981e-03, size: 256, ETA: 3:36:06
2025-08-01 02:48:54.236 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.981e-03, size: 544, ETA: 3:36:02
2025-08-01 02:48:55.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:49:02.966 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:49:04.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:49:06.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4538
2025-08-01 02:49:06.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3873
2025-08-01 02:49:06.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2068
2025-08-01 02:49:06.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3493
2025-08-01 02:49:06.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:49:06.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:49:06.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 02:49:06.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 02:49:06.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.207
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.349
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:49:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:49:07.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:49:09.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:49:10.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:49:12.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:49:13.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:49:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:49:16.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:49:18.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:49:19.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:49:19.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 02:49:19.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 02:49:19.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:49:19.803 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.87 ms, Average inference time: 8.36 ms

2025-08-01 02:49:19.804 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:49:19.883 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:49:19.973 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch48
2025-08-01 02:49:23.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.980e-03, size: 384, ETA: 3:35:57
2025-08-01 02:49:27.477 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.9, lr: 1.980e-03, size: 288, ETA: 3:35:56
2025-08-01 02:49:31.279 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, 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: 1.980e-03, size: 480, ETA: 3:35:53
2025-08-01 02:49:35.161 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.980e-03, size: 448, ETA: 3:35:51
2025-08-01 02:49:38.804 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.980e-03, size: 384, ETA: 3:35:47
2025-08-01 02:49:42.590 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.980e-03, size: 416, ETA: 3:35:44
2025-08-01 02:49:44.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:49:51.114 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:49:54.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:49:56.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4570
2025-08-01 02:49:56.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4021
2025-08-01 02:49:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2600
2025-08-01 02:49:56.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3730
2025-08-01 02:49:56.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:49:56.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.260
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.373
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:49:56.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:49:56.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:49:59.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:50:01.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:50:04.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:50:07.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:50:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:50:12.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:50:14.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:50:17.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:50:19.875 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:50:19.876 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 02:50:19.876 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 02:50:19.876 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:50:19.905 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.86 ms, Average inference time: 8.30 ms

2025-08-01 02:50:19.906 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:50:19.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:50:20.066 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch49
2025-08-01 02:50:23.689 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.176s, 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: 1.979e-03, size: 416, ETA: 3:35:37
2025-08-01 02:50:27.324 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.979e-03, size: 384, ETA: 3:35:32
2025-08-01 02:50:30.984 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.979e-03, size: 480, ETA: 3:35:29
2025-08-01 02:50:34.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, 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: 1.979e-03, size: 384, ETA: 3:35:25
2025-08-01 02:50:38.387 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.979e-03, size: 384, ETA: 3:35:21
2025-08-01 02:50:41.964 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.979e-03, size: 256, ETA: 3:35:16
2025-08-01 02:50:43.577 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:50:50.278 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:50:51.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:50:52.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2293
2025-08-01 02:50:52.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1597
2025-08-01 02:50:53.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1042
2025-08-01 02:50:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1644
2025-08-01 02:50:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:50:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.229
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.160
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.104
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.164
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:50:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:50:53.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:50:54.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:50:55.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:50:56.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:50:57.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:50:59.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:51:00.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:51:01.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:51:02.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:51:04.053 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:51:04.053 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.05
2025-08-01 02:51:04.053 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.16
2025-08-01 02:51:04.053 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:51:04.062 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.86 ms, Average inference time: 8.38 ms

2025-08-01 02:51:04.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:51:04.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:51:04.282 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch50
2025-08-01 02:51:08.145 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.189s, 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: 1.978e-03, size: 480, ETA: 3:35:12
2025-08-01 02:51:11.917 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.978e-03, size: 416, ETA: 3:35:09
2025-08-01 02:51:15.566 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.978e-03, size: 512, ETA: 3:35:05
2025-08-01 02:51:19.304 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.978e-03, size: 512, ETA: 3:35:02
2025-08-01 02:51:23.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.978e-03, size: 416, ETA: 3:35:01
2025-08-01 02:51:26.944 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.1, lr: 1.977e-03, size: 352, ETA: 3:34:58
2025-08-01 02:51:28.462 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:51:35.209 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:51:37.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:51:38.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4865
2025-08-01 02:51:39.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4399
2025-08-01 02:51:39.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2952
2025-08-01 02:51:39.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4072
2025-08-01 02:51:39.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:51:39.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:51:39.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-01 02:51:39.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-08-01 02:51:39.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.407
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:51:39.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:51:41.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:51:43.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:51:44.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:51:46.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:51:48.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:51:50.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:51:52.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:51:53.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:51:55.623 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:51:55.623 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 02:51:55.623 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 02:51:55.623 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:51:55.649 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.88 ms, Average inference time: 8.24 ms

2025-08-01 02:51:55.652 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:51:55.779 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:51:55.866 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch51
2025-08-01 02:51:59.232 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.977e-03, size: 384, ETA: 3:34:47
2025-08-01 02:52:02.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.977e-03, size: 288, ETA: 3:34:42
2025-08-01 02:52:06.764 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.196s, data_time: 0.002s, total_loss: 5.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.977e-03, size: 576, ETA: 3:34:42
2025-08-01 02:52:10.476 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.977e-03, size: 256, ETA: 3:34:38
2025-08-01 02:52:14.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.977e-03, size: 352, ETA: 3:34:34
2025-08-01 02:52:17.786 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.976e-03, size: 480, ETA: 3:34:31
2025-08-01 02:52:19.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:52:26.380 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:52:29.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:52:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4147
2025-08-01 02:52:30.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3646
2025-08-01 02:52:30.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1954
2025-08-01 02:52:30.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3249
2025-08-01 02:52:30.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:52:30.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:52:30.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-01 02:52:30.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-08-01 02:52:30.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.325
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:52:30.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:52:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:52:34.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:52:36.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:52:37.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:52:39.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:52:41.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:52:43.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:52:44.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:52:46.722 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:52:46.723 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 02:52:46.723 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 02:52:46.723 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:52:46.747 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.87 ms, Average inference time: 8.37 ms

2025-08-01 02:52:46.749 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:52:46.826 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:52:46.910 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch52
2025-08-01 02:52:50.457 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.175s, 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: 1.976e-03, size: 320, ETA: 3:34:24
2025-08-01 02:52:54.457 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.196s, 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: 1.976e-03, size: 512, ETA: 3:34:24
2025-08-01 02:52:58.352 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.192s, 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: 1.976e-03, size: 576, ETA: 3:34:22
2025-08-01 02:53:02.249 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.193s, 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: 1.976e-03, size: 288, ETA: 3:34:21
2025-08-01 02:53:05.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.174s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.975e-03, size: 320, ETA: 3:34:16
2025-08-01 02:53:09.633 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.187s, 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: 1.975e-03, size: 448, ETA: 3:34:13
2025-08-01 02:53:11.211 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:53:18.203 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:53:23.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:53:26.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1789
2025-08-01 02:53:26.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2071
2025-08-01 02:53:26.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0924
2025-08-01 02:53:26.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1595
2025-08-01 02:53:26.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:53:26.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:53:26.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.179
2025-08-01 02:53:26.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.207
2025-08-01 02:53:26.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.092
2025-08-01 02:53:26.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.159
2025-08-01 02:53:26.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:53:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:53:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:53:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:53:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:53:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:53:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:53:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:53:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:53:30.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:53:34.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:53:38.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:53:42.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:53:46.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:53:50.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:53:54.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:53:57.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:54:01.830 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:54:01.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.06
2025-08-01 02:54:01.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.16
2025-08-01 02:54:01.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:54:01.858 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.88 ms, Average inference time: 8.29 ms

2025-08-01 02:54:01.859 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:54:01.991 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:54:02.079 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch53
2025-08-01 02:54:05.353 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.161s, 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: 1.975e-03, size: 352, ETA: 3:34:02
2025-08-01 02:54:09.012 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.179s, 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: 1.975e-03, size: 480, ETA: 3:33:58
2025-08-01 02:54:12.815 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.975e-03, size: 384, ETA: 3:33:56
2025-08-01 02:54:16.578 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, 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: 1.974e-03, size: 352, ETA: 3:33:52
2025-08-01 02:54:20.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.974e-03, size: 384, ETA: 3:33:48
2025-08-01 02:54:24.013 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.974e-03, size: 576, ETA: 3:33:45
2025-08-01 02:54:25.671 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:54:32.482 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:54:35.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:54:37.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3912
2025-08-01 02:54:37.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3373
2025-08-01 02:54:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1350
2025-08-01 02:54:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2878
2025-08-01 02:54:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:54:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:54:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-01 02:54:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-08-01 02:54:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.135
2025-08-01 02:54:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.288
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:54:37.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:54:39.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:54:41.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:54:43.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:54:44.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:54:46.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:54:48.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:54:50.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:54:52.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:54:53.911 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:54:53.912 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 02:54:53.912 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 02:54:53.912 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:54:53.939 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.85 ms, Average inference time: 8.29 ms

2025-08-01 02:54:53.940 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:54:54.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:54:54.161 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch54
2025-08-01 02:54:57.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, 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: 1.974e-03, size: 288, ETA: 3:33:39
2025-08-01 02:55:01.568 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, 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: 1.974e-03, size: 448, ETA: 3:33:35
2025-08-01 02:55:05.301 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.973e-03, size: 352, ETA: 3:33:31
2025-08-01 02:55:08.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.973e-03, size: 256, ETA: 3:33:27
2025-08-01 02:55:12.504 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.973e-03, size: 352, ETA: 3:33:22
2025-08-01 02:55:16.111 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.973e-03, size: 448, ETA: 3:33:17
2025-08-01 02:55:17.699 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:55:24.660 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:55:28.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:55:31.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3559
2025-08-01 02:55:32.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3007
2025-08-01 02:55:32.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1242
2025-08-01 02:55:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2603
2025-08-01 02:55:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:55:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:55:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-01 02:55:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-08-01 02:55:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.124
2025-08-01 02:55:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.260
2025-08-01 02:55:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:55:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:55:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:55:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:55:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:55:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:55:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:55:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:55:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:55:35.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:55:39.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:55:42.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:55:46.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:55:49.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:55:53.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:55:56.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:55:59.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:56:03.303 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:56:03.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 02:56:03.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 02:56:03.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:56:03.332 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.87 ms, Average inference time: 8.38 ms

2025-08-01 02:56:03.333 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:56:03.469 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:56:03.562 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch55
2025-08-01 02:56:06.880 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, 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: 1.972e-03, size: 320, ETA: 3:33:07
2025-08-01 02:56:10.546 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.972e-03, size: 416, ETA: 3:33:03
2025-08-01 02:56:14.287 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, 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: 1.972e-03, size: 512, ETA: 3:33:00
2025-08-01 02:56:18.053 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.972e-03, size: 512, ETA: 3:32:56
2025-08-01 02:56:21.792 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.2, lr: 1.972e-03, size: 544, ETA: 3:32:53
2025-08-01 02:56:25.606 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.972e-03, size: 576, ETA: 3:32:50
2025-08-01 02:56:27.371 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:56:34.317 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:56:39.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:56:43.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3384
2025-08-01 02:56:43.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4126
2025-08-01 02:56:43.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1760
2025-08-01 02:56:43.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3090
2025-08-01 02:56:43.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:56:43.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:56:43.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-08-01 02:56:43.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 02:56:43.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.176
2025-08-01 02:56:43.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-08-01 02:56:43.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:56:43.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:56:43.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:56:43.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:56:43.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:56:43.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:56:43.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:56:43.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:56:43.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:56:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:56:53.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:56:57.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:57:02.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:57:06.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:57:11.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:57:15.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:57:20.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:57:24.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:57:24.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 02:57:24.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 02:57:24.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:57:24.850 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.88 ms, Average inference time: 8.36 ms

2025-08-01 02:57:24.852 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:57:24.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:57:25.017 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch56
2025-08-01 02:57:28.546 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.173s, 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: 1.971e-03, size: 384, ETA: 3:32:45
2025-08-01 02:57:32.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.195s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.971e-03, size: 576, ETA: 3:32:44
2025-08-01 02:57:36.455 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.971e-03, size: 576, ETA: 3:32:42
2025-08-01 02:57:40.380 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.971e-03, size: 416, ETA: 3:32:41
2025-08-01 02:57:43.991 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, 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: 1.970e-03, size: 288, ETA: 3:32:36
2025-08-01 02:57:47.483 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.172s, 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: 1.970e-03, size: 256, ETA: 3:32:31
2025-08-01 02:57:49.064 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:57:55.902 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:57:57.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:57:58.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3967
2025-08-01 02:57:58.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3164
2025-08-01 02:57:58.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1926
2025-08-01 02:57:58.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3019
2025-08-01 02:57:58.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:57:58.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:57:58.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-01 02:57:58.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-01 02:57:58.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.302
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:57:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:57:59.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:58:01.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:58:02.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:58:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:58:04.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:58:06.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:58:07.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:58:08.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:58:09.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:58:09.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 02:58:09.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 02:58:09.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:58:09.526 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.85 ms, Average inference time: 8.26 ms

2025-08-01 02:58:09.528 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:58:09.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:58:09.680 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch57
2025-08-01 02:58:13.228 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.970e-03, size: 352, ETA: 3:32:23
2025-08-01 02:58:17.173 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.970e-03, size: 576, ETA: 3:32:21
2025-08-01 02:58:21.144 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.197s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.970e-03, size: 480, ETA: 3:32:20
2025-08-01 02:58:24.749 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.969e-03, size: 384, ETA: 3:32:16
2025-08-01 02:58:28.411 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.969e-03, size: 480, ETA: 3:32:11
2025-08-01 02:58:32.152 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.969e-03, size: 576, ETA: 3:32:08
2025-08-01 02:58:33.919 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:58:40.811 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:58:44.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:58:48.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4388
2025-08-01 02:58:48.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4264
2025-08-01 02:58:48.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2665
2025-08-01 02:58:48.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3772
2025-08-01 02:58:48.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:58:48.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:58:48.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 02:58:48.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-08-01 02:58:48.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-08-01 02:58:48.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.377
2025-08-01 02:58:48.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:58:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:58:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:58:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:58:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:58:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:58:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:58:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:58:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 02:58:52.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 02:58:56.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 02:58:59.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 02:59:03.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 02:59:07.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 02:59:10.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 02:59:14.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 02:59:18.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 02:59:22.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 02:59:22.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 02:59:22.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 02:59:22.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 02:59:22.072 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.86 ms, Average inference time: 8.39 ms

2025-08-01 02:59:22.075 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:59:22.148 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:59:22.231 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch58
2025-08-01 02:59:26.050 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.969e-03, size: 544, ETA: 3:32:05
2025-08-01 02:59:29.894 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.968e-03, size: 288, ETA: 3:32:03
2025-08-01 02:59:33.465 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.968e-03, size: 320, ETA: 3:31:57
2025-08-01 02:59:37.125 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.968e-03, size: 256, ETA: 3:31:54
2025-08-01 02:59:40.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.005s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.968e-03, size: 448, ETA: 3:31:50
2025-08-01 02:59:44.575 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, 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: 1.968e-03, size: 512, ETA: 3:31:47
2025-08-01 02:59:46.242 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 02:59:52.881 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 02:59:55.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 02:59:57.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4904
2025-08-01 02:59:58.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4085
2025-08-01 02:59:58.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2241
2025-08-01 02:59:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3744
2025-08-01 02:59:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 02:59:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 02:59:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-01 02:59:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-08-01 02:59:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-08-01 02:59:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.374
2025-08-01 02:59:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 02:59:58.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 02:59:58.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 02:59:58.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 02:59:58.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 02:59:58.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 02:59:58.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 02:59:58.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 02:59:58.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:00:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:00:02.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:00:05.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:00:07.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:00:09.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:00:11.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:00:14.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:00:16.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:00:18.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:00:18.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 03:00:18.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:00:18.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:00:18.552 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.86 ms, Average inference time: 8.23 ms

2025-08-01 03:00:18.557 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:00:18.630 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:00:18.714 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch59
2025-08-01 03:00:22.437 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.181s, 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: 1.967e-03, size: 352, ETA: 3:31:42
2025-08-01 03:00:26.060 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.967e-03, size: 384, ETA: 3:31:38
2025-08-01 03:00:29.917 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.967e-03, size: 512, ETA: 3:31:36
2025-08-01 03:00:33.536 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, 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: 1.967e-03, size: 256, ETA: 3:31:31
2025-08-01 03:00:37.164 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.966e-03, size: 480, ETA: 3:31:26
2025-08-01 03:00:40.981 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, 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: 1.966e-03, size: 416, ETA: 3:31:23
2025-08-01 03:00:42.689 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:00:49.363 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:00:50.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:00:50.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4551
2025-08-01 03:00:50.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3844
2025-08-01 03:00:50.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2274
2025-08-01 03:00:50.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3556
2025-08-01 03:00:50.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:00:50.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:00:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:00:50.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:00:50.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:00:50.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:00:51.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:00:51.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:00:52.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:00:52.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:00:53.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:00:54.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:00:54.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:00:55.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:00:55.616 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:00:55.616 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 03:00:55.616 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:00:55.617 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:00:55.623 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.85 ms, Average inference time: 8.23 ms

2025-08-01 03:00:55.625 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:00:55.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:00:55.785 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch60
2025-08-01 03:00:59.720 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.196s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.966e-03, size: 512, ETA: 3:31:21
2025-08-01 03:01:03.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.190s, 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: 1.966e-03, size: 416, ETA: 3:31:19
2025-08-01 03:01:07.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.966e-03, size: 544, ETA: 3:31:14
2025-08-01 03:01:11.000 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.190s, 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: 1.965e-03, size: 448, ETA: 3:31:12
2025-08-01 03:01:14.777 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.965e-03, size: 352, ETA: 3:31:08
2025-08-01 03:01:18.391 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.180s, 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: 1.965e-03, size: 512, ETA: 3:31:04
2025-08-01 03:01:20.122 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:01:26.784 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:01:27.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:01:28.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4236
2025-08-01 03:01:28.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3723
2025-08-01 03:01:28.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2174
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3378
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.338
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:01:28.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:01:28.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:01:28.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:01:28.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:01:28.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:01:28.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:01:28.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:01:29.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:01:29.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:01:30.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:01:31.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:01:32.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:01:32.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:01:33.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:01:34.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:01:35.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:01:35.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 03:01:35.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 03:01:35.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:01:35.065 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.85 ms, Average inference time: 8.30 ms

2025-08-01 03:01:35.073 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:01:35.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:01:35.228 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch61
2025-08-01 03:01:38.765 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.965e-03, size: 384, ETA: 3:30:59
2025-08-01 03:01:42.333 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.176s, 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: 1.964e-03, size: 416, ETA: 3:30:54
2025-08-01 03:01:46.133 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.187s, 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: 1.964e-03, size: 512, ETA: 3:30:51
2025-08-01 03:01:50.129 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.198s, 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: 1.964e-03, size: 576, ETA: 3:30:51
2025-08-01 03:01:53.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.188s, 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: 1.964e-03, size: 576, ETA: 3:30:48
2025-08-01 03:01:57.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.963e-03, size: 256, ETA: 3:30:44
2025-08-01 03:01:59.507 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:02:06.278 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:02:07.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:02:08.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4423
2025-08-01 03:02:08.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4148
2025-08-01 03:02:08.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2274
2025-08-01 03:02:08.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3615
2025-08-01 03:02:08.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:02:08.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:02:08.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-01 03:02:08.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-01 03:02:08.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-08-01 03:02:08.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.362
2025-08-01 03:02:08.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:02:09.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:02:10.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:02:11.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:02:11.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:02:12.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:02:13.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:02:14.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:02:15.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:02:16.293 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:02:16.293 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:02:16.293 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:02:16.293 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:02:16.301 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.89 ms, Average inference time: 8.33 ms

2025-08-01 03:02:16.303 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:02:16.376 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:02:16.459 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch62
2025-08-01 03:02:20.033 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.175s, 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: 1.963e-03, size: 256, ETA: 3:30:40
2025-08-01 03:02:23.842 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, 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: 1.963e-03, size: 448, ETA: 3:30:37
2025-08-01 03:02:27.596 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.963e-03, size: 448, ETA: 3:30:34
2025-08-01 03:02:31.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, 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: 1.962e-03, size: 512, ETA: 3:30:30
2025-08-01 03:02:35.035 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.962e-03, size: 352, ETA: 3:30:27
2025-08-01 03:02:38.736 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.178s, 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: 1.962e-03, size: 512, ETA: 3:30:23
2025-08-01 03:02:40.432 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:02:47.196 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:02:48.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:02:48.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4365
2025-08-01 03:02:49.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3759
2025-08-01 03:02:49.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1673
2025-08-01 03:02:49.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3266
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.167
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.327
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:02:49.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:02:49.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:02:49.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:02:49.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:02:49.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:02:49.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:02:50.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:02:50.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:02:51.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:02:52.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:02:53.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:02:54.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:02:55.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:02:55.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:02:56.673 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:02:56.674 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:02:56.674 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 03:02:56.674 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:02:56.681 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.86 ms, Average inference time: 8.36 ms

2025-08-01 03:02:56.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:02:56.756 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:02:56.838 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch63
2025-08-01 03:03:00.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.3, lr: 1.962e-03, size: 288, ETA: 3:30:17
2025-08-01 03:03:04.071 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.180s, 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: 1.961e-03, size: 544, ETA: 3:30:13
2025-08-01 03:03:07.812 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.961e-03, size: 480, ETA: 3:30:09
2025-08-01 03:03:11.565 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.185s, 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: 1.961e-03, size: 544, ETA: 3:30:06
2025-08-01 03:03:15.192 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.961e-03, size: 416, ETA: 3:30:02
2025-08-01 03:03:18.732 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.961e-03, size: 256, ETA: 3:29:57
2025-08-01 03:03:20.547 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:03:27.450 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:03:29.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:03:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3966
2025-08-01 03:03:30.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3430
2025-08-01 03:03:30.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2184
2025-08-01 03:03:30.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3193
2025-08-01 03:03:30.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:03:30.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:03:30.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-01 03:03:30.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-08-01 03:03:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.218
2025-08-01 03:03:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.319
2025-08-01 03:03:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:03:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:03:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:03:30.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:03:30.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:03:30.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:03:30.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:03:30.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:03:30.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:03:31.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:03:33.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:03:34.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:03:35.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:03:37.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:03:38.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:03:39.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:03:41.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:03:42.465 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:03:42.465 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 03:03:42.465 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 03:03:42.465 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:03:42.473 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.87 ms, Average inference time: 8.30 ms

2025-08-01 03:03:42.474 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:03:42.546 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:03:42.628 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch64
2025-08-01 03:03:46.220 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.177s, 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: 1.960e-03, size: 288, ETA: 3:29:52
2025-08-01 03:03:50.006 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.960e-03, size: 512, ETA: 3:29:49
2025-08-01 03:03:53.982 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.195s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.960e-03, size: 288, ETA: 3:29:47
2025-08-01 03:03:57.772 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.960e-03, size: 448, ETA: 3:29:44
2025-08-01 03:04:01.630 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.959e-03, size: 448, ETA: 3:29:42
2025-08-01 03:04:05.405 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.184s, 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: 1.959e-03, size: 448, ETA: 3:29:38
2025-08-01 03:04:07.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:04:13.926 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:04:16.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:04:18.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5267
2025-08-01 03:04:18.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4298
2025-08-01 03:04:18.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2510
2025-08-01 03:04:18.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4025
2025-08-01 03:04:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:04:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:04:18.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-01 03:04:18.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-08-01 03:04:18.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.251
2025-08-01 03:04:18.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.403
2025-08-01 03:04:18.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:04:18.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:04:18.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:04:18.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:04:18.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:04:18.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:04:18.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:04:18.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:04:18.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:04:21.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:04:23.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:04:25.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:04:27.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:04:30.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:04:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:04:34.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:04:37.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:04:39.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:04:39.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 03:04:39.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 03:04:39.279 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:04:39.306 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.87 ms, Average inference time: 8.39 ms

2025-08-01 03:04:39.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:04:39.384 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:04:39.469 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch65
2025-08-01 03:04:43.043 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.959e-03, size: 416, ETA: 3:29:33
2025-08-01 03:04:46.762 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.958e-03, size: 448, ETA: 3:29:30
2025-08-01 03:04:50.588 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.187s, 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: 1.958e-03, size: 576, ETA: 3:29:27
2025-08-01 03:04:54.592 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.199s, 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: 1.958e-03, size: 384, ETA: 3:29:26
2025-08-01 03:04:58.466 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.190s, data_time: 0.005s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.958e-03, size: 384, ETA: 3:29:24
2025-08-01 03:05:02.215 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.958e-03, size: 448, ETA: 3:29:20
2025-08-01 03:05:03.818 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:05:10.770 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:05:16.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:05:19.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5201
2025-08-01 03:05:20.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4614
2025-08-01 03:05:20.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2560
2025-08-01 03:05:20.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4125
2025-08-01 03:05:20.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:05:20.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:05:20.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.256
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.413
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:05:20.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:05:25.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:05:30.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:05:34.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:05:39.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:05:44.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:05:48.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:05:53.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:05:57.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:06:02.612 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:06:02.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 03:06:02.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 03:06:02.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:06:02.639 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.89 ms, Average inference time: 8.39 ms

2025-08-01 03:06:02.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:06:02.719 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:06:02.804 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch66
2025-08-01 03:06:06.501 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.957e-03, size: 576, ETA: 3:29:14
2025-08-01 03:06:10.211 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, 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: 1.957e-03, size: 480, ETA: 3:29:10
2025-08-01 03:06:13.917 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.181s, 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: 1.957e-03, size: 448, ETA: 3:29:07
2025-08-01 03:06:17.502 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.956e-03, size: 480, ETA: 3:29:02
2025-08-01 03:06:21.324 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.190s, data_time: 0.005s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.956e-03, size: 576, ETA: 3:29:00
2025-08-01 03:06:25.049 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.956e-03, size: 480, ETA: 3:28:57
2025-08-01 03:06:26.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:06:33.453 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:06:35.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:06:36.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4844
2025-08-01 03:06:36.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4042
2025-08-01 03:06:36.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2307
2025-08-01 03:06:36.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3731
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.231
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.373
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:06:36.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:06:36.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:06:36.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:06:36.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:06:36.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:06:36.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:06:36.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:06:38.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:06:40.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:06:41.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:06:43.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:06:45.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:06:46.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:06:48.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:06:50.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:06:51.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:06:51.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:06:51.632 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:06:51.632 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:06:51.658 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.30 ms, Average NMS time: 0.87 ms, Average inference time: 8.17 ms

2025-08-01 03:06:51.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:06:51.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:06:51.876 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch67
2025-08-01 03:06:55.586 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.956e-03, size: 256, ETA: 3:28:51
2025-08-01 03:06:59.148 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.955e-03, size: 256, ETA: 3:28:46
2025-08-01 03:07:02.960 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, 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: 1.955e-03, size: 320, ETA: 3:28:43
2025-08-01 03:07:06.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.955e-03, size: 320, ETA: 3:28:41
2025-08-01 03:07:10.485 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 1.0, lr: 1.955e-03, size: 256, ETA: 3:28:37
2025-08-01 03:07:14.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.954e-03, size: 320, ETA: 3:28:33
2025-08-01 03:07:15.807 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:07:22.470 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:07:23.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:07:23.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4176
2025-08-01 03:07:23.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3823
2025-08-01 03:07:23.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2386
2025-08-01 03:07:23.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3462
2025-08-01 03:07:23.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.239
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.346
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:07:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:07:23.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:07:23.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:07:24.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:07:24.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:07:25.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:07:25.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:07:26.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:07:26.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:07:27.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:07:27.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:07:28.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:07:28.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 03:07:28.335 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 03:07:28.335 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:07:28.342 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.83 ms, Average inference time: 8.21 ms

2025-08-01 03:07:28.344 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:07:28.472 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:07:28.560 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch68
2025-08-01 03:07:32.248 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.181s, 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: 1.954e-03, size: 352, ETA: 3:28:28
2025-08-01 03:07:35.977 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, 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: 1.954e-03, size: 480, ETA: 3:28:24
2025-08-01 03:07:39.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.192s, 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: 1.954e-03, size: 512, ETA: 3:28:22
2025-08-01 03:07:43.506 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.953e-03, size: 384, ETA: 3:28:18
2025-08-01 03:07:47.244 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.186s, 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: 1.953e-03, size: 448, ETA: 3:28:15
2025-08-01 03:07:51.003 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.185s, 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: 1.953e-03, size: 288, ETA: 3:28:12
2025-08-01 03:07:52.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:07:59.424 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:08:05.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:08:10.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4875
2025-08-01 03:08:10.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3991
2025-08-01 03:08:10.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2495
2025-08-01 03:08:10.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3787
2025-08-01 03:08:10.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:08:10.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:08:10.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.379
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:08:10.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:08:10.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:08:14.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:08:19.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:08:23.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:08:28.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:08:32.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:08:37.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:08:41.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:08:45.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:08:50.363 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:08:50.363 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 03:08:50.363 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 03:08:50.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:08:50.392 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.87 ms, Average inference time: 8.24 ms

2025-08-01 03:08:50.393 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:08:50.477 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:08:50.568 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch69
2025-08-01 03:08:53.998 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.952e-03, size: 544, ETA: 3:28:03
2025-08-01 03:08:57.709 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.952e-03, size: 352, ETA: 3:28:00
2025-08-01 03:09:01.443 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.952e-03, size: 256, ETA: 3:27:56
2025-08-01 03:09:04.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.169s, 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: 1.952e-03, size: 448, ETA: 3:27:51
2025-08-01 03:09:08.869 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.951e-03, size: 352, ETA: 3:27:49
2025-08-01 03:09:12.421 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.951e-03, size: 384, ETA: 3:27:44
2025-08-01 03:09:14.128 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:09:20.848 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:09:22.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:09:23.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4511
2025-08-01 03:09:23.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3633
2025-08-01 03:09:23.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2635
2025-08-01 03:09:23.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3593
2025-08-01 03:09:23.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:09:23.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:09:23.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 03:09:23.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-01 03:09:23.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-08-01 03:09:23.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-08-01 03:09:23.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:09:23.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:09:23.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:09:23.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:09:23.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:09:23.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:09:23.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:09:23.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:09:23.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:09:24.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:09:25.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:09:26.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:09:27.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:09:27.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:09:28.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:09:29.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:09:30.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:09:31.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:09:31.543 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:09:31.543 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:09:31.543 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:09:31.551 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.29 ms, Average NMS time: 0.84 ms, Average inference time: 8.13 ms

2025-08-01 03:09:31.553 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:09:31.632 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:09:31.716 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch70
2025-08-01 03:09:35.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.951e-03, size: 480, ETA: 3:27:40
2025-08-01 03:09:39.442 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.951e-03, size: 256, ETA: 3:27:38
2025-08-01 03:09:43.024 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.176s, 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: 1.950e-03, size: 544, ETA: 3:27:33
2025-08-01 03:09:46.901 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, 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: 1.950e-03, size: 320, ETA: 3:27:31
2025-08-01 03:09:50.882 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.197s, data_time: 0.006s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.950e-03, size: 384, ETA: 3:27:30
2025-08-01 03:09:54.698 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.950e-03, size: 416, ETA: 3:27:27
2025-08-01 03:09:56.300 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:10:03.053 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:10:05.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:10:06.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4416
2025-08-01 03:10:06.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4052
2025-08-01 03:10:06.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2060
2025-08-01 03:10:06.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3509
2025-08-01 03:10:06.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:10:06.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.206
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.351
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:10:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:10:06.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:10:08.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:10:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:10:12.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:10:13.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:10:15.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:10:17.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:10:18.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:10:20.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:10:22.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:10:22.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 03:10:22.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 03:10:22.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:10:22.205 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.86 ms, Average inference time: 8.31 ms

2025-08-01 03:10:22.206 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:10:22.283 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:10:22.435 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch71
2025-08-01 03:10:25.988 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.949e-03, size: 352, ETA: 3:27:20
2025-08-01 03:10:29.655 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.949e-03, size: 256, ETA: 3:27:16
2025-08-01 03:10:33.374 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.949e-03, size: 416, ETA: 3:27:13
2025-08-01 03:10:36.991 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.948e-03, size: 320, ETA: 3:27:09
2025-08-01 03:10:40.895 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.194s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.948e-03, size: 320, ETA: 3:27:07
2025-08-01 03:10:44.638 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.948e-03, size: 544, ETA: 3:27:04
2025-08-01 03:10:46.278 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:10:53.185 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:10:54.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:10:55.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3733
2025-08-01 03:10:56.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3542
2025-08-01 03:10:56.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1398
2025-08-01 03:10:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2891
2025-08-01 03:10:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:10:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:10:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-01 03:10:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-08-01 03:10:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.140
2025-08-01 03:10:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.289
2025-08-01 03:10:56.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:10:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:10:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:10:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:10:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:10:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:10:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:10:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:10:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:10:57.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:10:58.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:11:00.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:11:01.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:11:02.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:11:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:11:05.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:11:06.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:11:07.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:11:07.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:11:07.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 03:11:07.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:11:07.781 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.86 ms, Average inference time: 8.33 ms

2025-08-01 03:11:07.784 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:11:07.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:11:07.990 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch72
2025-08-01 03:11:11.619 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.947e-03, size: 448, ETA: 3:26:58
2025-08-01 03:11:15.159 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.947e-03, size: 256, ETA: 3:26:53
2025-08-01 03:11:19.164 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.198s, 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: 1.947e-03, size: 384, ETA: 3:26:52
2025-08-01 03:11:22.770 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.947e-03, size: 256, ETA: 3:26:47
2025-08-01 03:11:26.622 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.190s, 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: 1.946e-03, size: 576, ETA: 3:26:45
2025-08-01 03:11:30.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, 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: 1.946e-03, size: 256, ETA: 3:26:41
2025-08-01 03:11:32.117 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:11:38.834 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:11:40.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:11:41.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3375
2025-08-01 03:11:41.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3338
2025-08-01 03:11:41.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1909
2025-08-01 03:11:41.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2874
2025-08-01 03:11:41.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:11:41.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:11:41.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-08-01 03:11:41.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-01 03:11:41.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 03:11:41.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.287
2025-08-01 03:11:41.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:11:41.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:11:41.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:11:41.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:11:41.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:11:41.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:11:41.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:11:41.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:11:41.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:11:42.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:11:44.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:11:45.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:11:46.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:11:47.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:11:48.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:11:49.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:11:51.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:11:52.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:11:52.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 03:11:52.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 03:11:52.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:11:52.307 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.86 ms, Average inference time: 8.22 ms

2025-08-01 03:11:52.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:11:52.469 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:11:52.561 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch73
2025-08-01 03:11:56.182 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.946e-03, size: 480, ETA: 3:26:36
2025-08-01 03:11:59.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.945e-03, size: 416, ETA: 3:26:33
2025-08-01 03:12:03.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.180s, 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: 1.945e-03, size: 480, ETA: 3:26:29
2025-08-01 03:12:07.440 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.185s, 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: 1.945e-03, size: 544, ETA: 3:26:25
2025-08-01 03:12:11.161 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, 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: 1.945e-03, size: 544, ETA: 3:26:22
2025-08-01 03:12:14.989 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.944e-03, size: 512, ETA: 3:26:19
2025-08-01 03:12:16.624 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:12:23.475 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:12:26.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:12:28.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4793
2025-08-01 03:12:29.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3758
2025-08-01 03:12:29.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2585
2025-08-01 03:12:29.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3712
2025-08-01 03:12:29.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.259
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:12:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:12:29.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:12:29.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:12:29.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:12:29.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:12:31.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:12:34.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:12:37.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:12:39.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:12:42.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:12:44.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:12:47.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:12:49.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:12:52.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:12:52.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 03:12:52.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:12:52.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:12:52.284 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.86 ms, Average inference time: 8.27 ms

2025-08-01 03:12:52.285 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:12:52.358 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:12:52.443 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch74
2025-08-01 03:12:55.906 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.944e-03, size: 288, ETA: 3:26:12
2025-08-01 03:12:59.526 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.944e-03, size: 256, ETA: 3:26:08
2025-08-01 03:13:03.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.943e-03, size: 448, ETA: 3:26:04
2025-08-01 03:13:06.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.187s, 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: 1.943e-03, size: 512, ETA: 3:26:01
2025-08-01 03:13:10.690 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.2, lr: 1.943e-03, size: 576, ETA: 3:25:58
2025-08-01 03:13:14.436 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.943e-03, size: 416, ETA: 3:25:54
2025-08-01 03:13:16.099 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:13:22.862 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:13:25.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:13:27.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3579
2025-08-01 03:13:28.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2903
2025-08-01 03:13:28.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1544
2025-08-01 03:13:28.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2675
2025-08-01 03:13:28.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:13:28.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:13:28.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-01 03:13:28.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.154
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.268
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:13:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:13:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:13:33.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:13:35.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:13:38.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:13:40.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:13:43.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:13:45.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:13:48.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:13:51.021 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:13:51.021 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 03:13:51.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 03:13:51.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:13:51.051 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.89 ms, Average inference time: 8.27 ms

2025-08-01 03:13:51.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:13:51.139 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:13:51.228 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch75
2025-08-01 03:13:54.799 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.942e-03, size: 416, ETA: 3:25:48
2025-08-01 03:13:58.389 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.177s, 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: 1.942e-03, size: 448, ETA: 3:25:43
2025-08-01 03:14:02.246 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.942e-03, size: 480, ETA: 3:25:41
2025-08-01 03:14:06.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, 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: 1.941e-03, size: 544, ETA: 3:25:38
2025-08-01 03:14:09.800 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, 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: 1.941e-03, size: 352, ETA: 3:25:35
2025-08-01 03:14:13.745 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.194s, 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: 1.941e-03, size: 512, ETA: 3:25:33
2025-08-01 03:14:15.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:14:22.142 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:14:24.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:14:26.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4740
2025-08-01 03:14:26.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4098
2025-08-01 03:14:26.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2376
2025-08-01 03:14:26.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3738
2025-08-01 03:14:26.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:14:26.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:14:26.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-01 03:14:26.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-01 03:14:26.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-08-01 03:14:26.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.374
2025-08-01 03:14:26.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:14:26.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:14:26.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:14:26.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:14:26.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:14:26.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:14:26.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:14:26.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:14:26.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:14:28.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:14:30.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:14:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:14:34.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:14:36.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:14:38.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:14:40.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:14:42.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:14:44.091 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:14:44.091 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:14:44.091 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:14:44.091 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:14:44.119 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.86 ms, Average inference time: 8.36 ms

2025-08-01 03:14:44.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:14:44.202 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:14:44.287 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch76
2025-08-01 03:14:47.987 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.9, lr: 1.940e-03, size: 576, ETA: 3:25:27
2025-08-01 03:14:51.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.194s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.940e-03, size: 544, ETA: 3:25:25
2025-08-01 03:14:55.654 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, 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: 1.940e-03, size: 544, ETA: 3:25:22
2025-08-01 03:14:59.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.940e-03, size: 352, ETA: 3:25:19
2025-08-01 03:15:03.009 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.939e-03, size: 544, ETA: 3:25:14
2025-08-01 03:15:06.942 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.190s, 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: 1.939e-03, size: 352, ETA: 3:25:12
2025-08-01 03:15:08.667 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:15:15.491 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:15:19.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:15:22.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4013
2025-08-01 03:15:22.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3430
2025-08-01 03:15:22.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1810
2025-08-01 03:15:22.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3084
2025-08-01 03:15:22.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:15:22.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:15:22.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-08-01 03:15:22.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-08-01 03:15:22.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-08-01 03:15:22.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.308
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:15:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:15:25.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:15:27.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:15:30.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:15:33.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:15:35.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:15:38.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:15:41.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:15:43.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:15:46.386 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:15:46.386 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:15:46.386 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 03:15:46.386 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:15:46.414 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.87 ms, Average inference time: 8.31 ms

2025-08-01 03:15:46.415 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:15:46.501 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:15:46.586 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch77
2025-08-01 03:15:50.155 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.939e-03, size: 384, ETA: 3:25:06
2025-08-01 03:15:53.912 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.186s, 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: 1.938e-03, size: 544, ETA: 3:25:03
2025-08-01 03:15:57.792 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.938e-03, size: 576, ETA: 3:25:00
2025-08-01 03:16:01.785 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.193s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.938e-03, size: 448, ETA: 3:24:58
2025-08-01 03:16:05.596 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.937e-03, size: 320, ETA: 3:24:55
2025-08-01 03:16:09.272 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.937e-03, size: 480, ETA: 3:24:51
2025-08-01 03:16:10.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:16:17.721 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:16:19.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:16:19.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4497
2025-08-01 03:16:20.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4185
2025-08-01 03:16:20.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2825
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3836
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.282
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.384
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:16:20.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:16:20.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:16:20.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:16:20.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:16:20.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:16:20.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:16:20.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:16:21.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:16:22.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:16:23.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:16:24.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:16:25.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:16:27.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:16:28.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:16:29.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:16:30.491 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:16:30.491 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 03:16:30.491 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 03:16:30.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:16:30.500 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.86 ms, Average inference time: 8.28 ms

2025-08-01 03:16:30.501 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:16:30.614 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:16:30.726 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch78
2025-08-01 03:16:34.361 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.937e-03, size: 256, ETA: 3:24:46
2025-08-01 03:16:38.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.177s, 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: 1.936e-03, size: 576, ETA: 3:24:41
2025-08-01 03:16:41.953 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.936e-03, size: 352, ETA: 3:24:39
2025-08-01 03:16:45.630 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.936e-03, size: 544, ETA: 3:24:35
2025-08-01 03:16:49.515 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.190s, 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: 1.936e-03, size: 320, ETA: 3:24:33
2025-08-01 03:16:53.061 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.935e-03, size: 512, ETA: 3:24:28
2025-08-01 03:16:54.808 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:17:01.707 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:17:04.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:17:07.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5300
2025-08-01 03:17:07.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4244
2025-08-01 03:17:07.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2683
2025-08-01 03:17:07.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4076
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.268
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.408
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:17:07.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:17:07.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:17:07.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:17:07.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:17:07.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:17:10.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:17:12.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:17:15.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:17:17.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:17:20.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:17:22.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:17:25.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:17:28.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:17:30.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:17:30.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 03:17:30.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 03:17:30.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:17:30.685 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.89 ms, Average inference time: 8.46 ms

2025-08-01 03:17:30.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:17:30.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:17:30.859 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch79
2025-08-01 03:17:34.387 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.935e-03, size: 544, ETA: 3:24:22
2025-08-01 03:17:38.167 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.187s, 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: 1.935e-03, size: 320, ETA: 3:24:19
2025-08-01 03:17:41.707 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.934e-03, size: 384, ETA: 3:24:14
2025-08-01 03:17:45.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.934e-03, size: 384, ETA: 3:24:11
2025-08-01 03:17:49.100 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.174s, 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: 1.934e-03, size: 352, ETA: 3:24:07
2025-08-01 03:17:52.909 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.933e-03, size: 576, ETA: 3:24:04
2025-08-01 03:17:54.696 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:18:01.718 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:18:04.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:18:07.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4902
2025-08-01 03:18:07.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4072
2025-08-01 03:18:07.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2391
2025-08-01 03:18:07.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3788
2025-08-01 03:18:07.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:18:07.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:18:07.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-01 03:18:07.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 03:18:07.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.239
2025-08-01 03:18:07.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.379
2025-08-01 03:18:07.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:18:07.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:18:07.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:18:07.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:18:07.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:18:07.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:18:07.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:18:07.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:18:07.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:18:10.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:18:13.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:18:15.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:18:18.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:18:21.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:18:24.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:18:27.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:18:29.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:18:32.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:18:32.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 03:18:32.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 03:18:32.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:18:32.554 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.87 ms, Average inference time: 8.32 ms

2025-08-01 03:18:32.555 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:18:32.642 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:18:32.733 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch80
2025-08-01 03:18:36.165 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.7, cls_loss: 1.3, lr: 1.933e-03, size: 576, ETA: 3:23:58
2025-08-01 03:18:40.124 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.195s, 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: 1.933e-03, size: 448, ETA: 3:23:56
2025-08-01 03:18:43.805 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.932e-03, size: 576, ETA: 3:23:52
2025-08-01 03:18:47.807 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.196s, 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: 1.932e-03, size: 448, ETA: 3:23:50
2025-08-01 03:18:51.677 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.932e-03, size: 512, ETA: 3:23:48
2025-08-01 03:18:55.374 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.931e-03, size: 448, ETA: 3:23:44
2025-08-01 03:18:57.140 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:19:03.879 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:19:05.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:19:06.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3926
2025-08-01 03:19:06.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3591
2025-08-01 03:19:06.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1914
2025-08-01 03:19:06.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3144
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.314
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:19:06.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:19:06.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:19:06.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:19:06.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:19:06.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:19:06.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:19:06.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:19:07.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:19:09.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:19:10.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:19:11.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:19:12.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:19:14.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:19:15.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:19:16.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:19:17.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:19:17.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:19:17.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 03:19:17.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:19:17.786 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.86 ms, Average inference time: 8.30 ms

2025-08-01 03:19:17.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:19:17.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:19:17.994 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch81
2025-08-01 03:19:21.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.190s, 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: 1.931e-03, size: 352, ETA: 3:23:41
2025-08-01 03:19:25.952 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.204s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.931e-03, size: 416, ETA: 3:23:40
2025-08-01 03:19:30.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.203s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.930e-03, size: 448, ETA: 3:23:39
2025-08-01 03:19:34.082 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.199s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.930e-03, size: 448, ETA: 3:23:37
2025-08-01 03:19:38.140 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.200s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.930e-03, size: 320, ETA: 3:23:36
2025-08-01 03:19:41.580 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.930e-03, size: 256, ETA: 3:23:31
2025-08-01 03:19:43.333 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:19:50.040 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:19:52.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:19:53.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5122
2025-08-01 03:19:53.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4523
2025-08-01 03:19:53.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2813
2025-08-01 03:19:53.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4152
2025-08-01 03:19:53.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:19:53.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:19:53.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-01 03:19:53.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 03:19:53.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.281
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.415
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:19:53.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:19:55.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:19:56.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:19:58.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:19:59.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:20:01.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:20:02.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:20:04.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:20:05.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:20:07.519 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:20:07.519 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 03:20:07.519 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 03:20:07.520 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:20:07.546 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.88 ms, Average inference time: 8.34 ms

2025-08-01 03:20:07.547 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:20:07.627 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:20:07.716 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch82
2025-08-01 03:20:11.532 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.929e-03, size: 576, ETA: 3:23:27
2025-08-01 03:20:15.183 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.929e-03, size: 384, ETA: 3:23:23
2025-08-01 03:20:18.976 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.5, cls_loss: 0.8, lr: 1.928e-03, size: 576, ETA: 3:23:19
2025-08-01 03:20:22.907 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.194s, 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: 1.928e-03, size: 512, ETA: 3:23:17
2025-08-01 03:20:26.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.928e-03, size: 576, ETA: 3:23:15
2025-08-01 03:20:30.732 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.193s, 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: 1.928e-03, size: 576, ETA: 3:23:13
2025-08-01 03:20:32.454 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:20:39.227 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:20:40.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:20:42.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3746
2025-08-01 03:20:42.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3451
2025-08-01 03:20:42.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1810
2025-08-01 03:20:42.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3002
2025-08-01 03:20:42.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:20:42.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:20:42.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-01 03:20:42.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-01 03:20:42.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-08-01 03:20:42.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.300
2025-08-01 03:20:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:20:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:20:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:20:42.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:20:42.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:20:42.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:20:42.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:20:42.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:20:42.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:20:44.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:20:45.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:20:46.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:20:48.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:20:49.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:20:51.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:20:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:20:54.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:20:55.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:20:55.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:20:55.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 03:20:55.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:20:55.918 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.86 ms, Average inference time: 8.17 ms

2025-08-01 03:20:55.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:20:55.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:20:56.077 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch83
2025-08-01 03:20:59.696 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.927e-03, size: 544, ETA: 3:23:07
2025-08-01 03:21:03.414 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.927e-03, size: 416, ETA: 3:23:03
2025-08-01 03:21:06.897 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.926e-03, size: 320, ETA: 3:22:58
2025-08-01 03:21:10.772 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.926e-03, size: 352, ETA: 3:22:55
2025-08-01 03:21:14.358 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.926e-03, size: 416, ETA: 3:22:51
2025-08-01 03:21:18.127 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, 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: 1.926e-03, size: 544, ETA: 3:22:47
2025-08-01 03:21:19.834 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:21:26.666 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:21:28.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:21:30.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4511
2025-08-01 03:21:30.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4107
2025-08-01 03:21:30.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2078
2025-08-01 03:21:30.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3565
2025-08-01 03:21:30.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:21:30.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:21:30.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.208
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.357
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:21:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:21:32.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:21:34.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:21:37.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:21:39.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:21:41.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:21:43.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:21:45.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:21:47.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:21:49.162 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:21:49.162 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 03:21:49.162 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:21:49.163 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:21:49.187 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.87 ms, Average inference time: 8.33 ms

2025-08-01 03:21:49.189 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:21:49.269 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:21:49.358 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch84
2025-08-01 03:21:53.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.7, cls_loss: 0.7, lr: 1.925e-03, size: 448, ETA: 3:22:43
2025-08-01 03:21:56.906 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.925e-03, size: 480, ETA: 3:22:39
2025-08-01 03:22:00.691 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.186s, 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: 1.924e-03, size: 416, ETA: 3:22:36
2025-08-01 03:22:04.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.178s, 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: 1.924e-03, size: 512, ETA: 3:22:32
2025-08-01 03:22:08.233 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.924e-03, size: 256, ETA: 3:22:29
2025-08-01 03:22:12.017 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.1, lr: 1.924e-03, size: 512, ETA: 3:22:26
2025-08-01 03:22:13.835 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:22:20.613 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:22:23.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:22:24.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4989
2025-08-01 03:22:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3643
2025-08-01 03:22:25.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2336
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3656
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.234
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.366
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:22:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:22:25.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:22:25.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:22:25.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:22:25.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:22:25.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:22:26.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:22:28.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:22:30.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:22:31.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:22:33.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:22:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:22:36.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:22:38.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:22:39.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:22:39.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:22:39.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:22:39.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:22:39.867 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.86 ms, Average inference time: 8.33 ms

2025-08-01 03:22:39.868 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:22:39.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:22:40.060 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch85
2025-08-01 03:22:43.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.923e-03, size: 256, ETA: 3:22:21
2025-08-01 03:22:47.355 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.181s, 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: 1.923e-03, size: 576, ETA: 3:22:17
2025-08-01 03:22:51.146 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.187s, 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: 1.922e-03, size: 256, ETA: 3:22:14
2025-08-01 03:22:54.998 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.922e-03, size: 352, ETA: 3:22:11
2025-08-01 03:22:58.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.922e-03, size: 320, ETA: 3:22:07
2025-08-01 03:23:02.465 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.921e-03, size: 416, ETA: 3:22:04
2025-08-01 03:23:04.211 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:23:11.090 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:23:12.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:23:13.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4044
2025-08-01 03:23:13.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3153
2025-08-01 03:23:13.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2286
2025-08-01 03:23:13.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3161
2025-08-01 03:23:13.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:23:13.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:23:13.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-01 03:23:13.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-08-01 03:23:13.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.229
2025-08-01 03:23:13.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.316
2025-08-01 03:23:13.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:23:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:23:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:23:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:23:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:23:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:23:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:23:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:23:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:23:14.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:23:15.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:23:16.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:23:17.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:23:18.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:23:19.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:23:20.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:23:21.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:23:21.988 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:23:21.988 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 03:23:21.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 03:23:21.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:23:21.996 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.83 ms, Average inference time: 8.34 ms

2025-08-01 03:23:21.998 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:23:22.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:23:22.180 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch86
2025-08-01 03:23:25.836 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.921e-03, size: 544, ETA: 3:21:58
2025-08-01 03:23:29.694 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.190s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.921e-03, size: 544, ETA: 3:21:55
2025-08-01 03:23:33.394 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, 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: 1.920e-03, size: 384, ETA: 3:21:51
2025-08-01 03:23:37.282 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.190s, 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: 1.920e-03, size: 544, ETA: 3:21:49
2025-08-01 03:23:41.073 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.187s, 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: 1.920e-03, size: 384, ETA: 3:21:45
2025-08-01 03:23:44.856 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.919e-03, size: 480, ETA: 3:21:42
2025-08-01 03:23:46.466 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:23:53.190 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:23:56.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:23:57.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4505
2025-08-01 03:23:58.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4253
2025-08-01 03:23:58.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1924
2025-08-01 03:23:58.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3561
2025-08-01 03:23:58.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:23:58.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:23:58.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 03:23:58.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-01 03:23:58.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.192
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:23:58.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:24:00.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:24:02.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:24:04.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:24:06.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:24:08.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:24:10.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:24:12.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:24:14.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:24:16.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:24:16.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 03:24:16.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:24:16.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:24:16.899 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.84 ms, Average inference time: 8.28 ms

2025-08-01 03:24:16.900 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:24:16.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:24:17.070 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch87
2025-08-01 03:24:20.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.171s, 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: 1.919e-03, size: 512, ETA: 3:21:35
2025-08-01 03:24:24.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.919e-03, size: 288, ETA: 3:21:32
2025-08-01 03:24:28.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.918e-03, size: 320, ETA: 3:21:28
2025-08-01 03:24:31.729 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.918e-03, size: 512, ETA: 3:21:24
2025-08-01 03:24:35.501 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.918e-03, size: 384, ETA: 3:21:21
2025-08-01 03:24:39.191 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.917e-03, size: 384, ETA: 3:21:17
2025-08-01 03:24:40.833 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:24:47.687 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:24:48.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:24:49.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3656
2025-08-01 03:24:49.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2911
2025-08-01 03:24:50.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1934
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2834
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.291
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.283
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:24:50.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:24:50.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:24:50.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:24:50.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:24:50.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:24:50.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:24:51.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:24:52.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:24:53.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:24:54.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:24:55.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:24:56.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:24:57.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:24:58.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:24:59.194 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:24:59.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 03:24:59.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 03:24:59.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:24:59.203 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.88 ms, Average inference time: 8.23 ms

2025-08-01 03:24:59.208 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:24:59.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:24:59.436 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch88
2025-08-01 03:25:03.123 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.917e-03, size: 320, ETA: 3:21:11
2025-08-01 03:25:06.870 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.916e-03, size: 352, ETA: 3:21:08
2025-08-01 03:25:10.484 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.178s, 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: 1.916e-03, size: 352, ETA: 3:21:03
2025-08-01 03:25:14.026 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.174s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.916e-03, size: 256, ETA: 3:20:59
2025-08-01 03:25:17.888 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.915e-03, size: 448, ETA: 3:20:56
2025-08-01 03:25:21.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, 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: 1.915e-03, size: 416, ETA: 3:20:52
2025-08-01 03:25:23.258 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:25:29.949 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:25:31.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:25:32.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4809
2025-08-01 03:25:32.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3895
2025-08-01 03:25:32.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2122
2025-08-01 03:25:32.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3608
2025-08-01 03:25:32.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:25:32.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:25:32.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 03:25:32.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-08-01 03:25:32.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.212
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:25:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:25:33.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:25:34.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:25:35.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:25:36.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:25:37.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:25:38.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:25:39.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:25:40.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:25:41.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:25:41.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 03:25:41.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:25:41.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:25:41.243 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.86 ms, Average inference time: 8.27 ms

2025-08-01 03:25:41.245 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:25:41.404 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:25:41.526 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch89
2025-08-01 03:25:45.151 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.2, lr: 1.915e-03, size: 256, ETA: 3:20:46
2025-08-01 03:25:48.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.914e-03, size: 480, ETA: 3:20:42
2025-08-01 03:25:52.594 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.188s, 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: 1.914e-03, size: 352, ETA: 3:20:39
2025-08-01 03:25:56.192 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.914e-03, size: 256, ETA: 3:20:35
2025-08-01 03:25:59.733 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.175s, 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: 1.913e-03, size: 576, ETA: 3:20:30
2025-08-01 03:26:03.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.194s, 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: 1.913e-03, size: 576, ETA: 3:20:28
2025-08-01 03:26:05.486 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:26:12.310 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:26:13.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:26:15.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4381
2025-08-01 03:26:15.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3728
2025-08-01 03:26:15.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2094
2025-08-01 03:26:15.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3401
2025-08-01 03:26:15.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:26:15.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:26:15.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-08-01 03:26:15.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.340
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:26:15.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:26:15.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:26:16.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:26:17.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:26:19.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:26:20.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:26:21.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:26:22.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:26:24.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:26:25.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:26:26.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:26:26.988 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:26:26.988 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 03:26:26.988 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:26:27.003 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.87 ms, Average inference time: 8.27 ms

2025-08-01 03:26:27.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:26:27.115 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:26:27.237 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch90
2025-08-01 03:26:30.924 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.178s, 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: 1.912e-03, size: 576, ETA: 3:20:23
2025-08-01 03:26:34.816 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.193s, 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: 1.912e-03, size: 544, ETA: 3:20:21
2025-08-01 03:26:38.760 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.193s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.912e-03, size: 416, ETA: 3:20:18
2025-08-01 03:26:42.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.186s, 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: 1.911e-03, size: 512, ETA: 3:20:15
2025-08-01 03:26:46.161 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.178s, 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: 1.911e-03, size: 416, ETA: 3:20:11
2025-08-01 03:26:49.771 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.911e-03, size: 416, ETA: 3:20:06
2025-08-01 03:26:51.401 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:26:58.361 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:27:01.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:27:03.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2869
2025-08-01 03:27:03.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2300
2025-08-01 03:27:03.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1520
2025-08-01 03:27:03.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2230
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.230
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.152
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.223
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:27:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:27:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:27:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:27:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:27:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:27:06.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:27:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:27:10.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:27:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:27:14.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:27:17.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:27:19.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:27:21.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:27:23.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:27:23.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-08-01 03:27:23.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-08-01 03:27:23.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:27:23.746 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.87 ms, Average inference time: 8.27 ms

2025-08-01 03:27:23.747 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:27:23.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:27:23.953 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch91
2025-08-01 03:27:27.653 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, 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: 1.910e-03, size: 544, ETA: 3:20:01
2025-08-01 03:27:31.348 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.910e-03, size: 384, ETA: 3:19:57
2025-08-01 03:27:34.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.8, lr: 1.910e-03, size: 320, ETA: 3:19:53
2025-08-01 03:27:38.641 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 6.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 1.0, lr: 1.909e-03, size: 448, ETA: 3:19:49
2025-08-01 03:27:42.401 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, 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: 1.909e-03, size: 544, ETA: 3:19:46
2025-08-01 03:27:46.230 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.909e-03, size: 544, ETA: 3:19:43
2025-08-01 03:27:47.917 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:27:54.622 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:27:55.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:27:56.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4193
2025-08-01 03:27:56.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3638
2025-08-01 03:27:56.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1589
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3140
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.159
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.314
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:27:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:27:56.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:27:56.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:27:56.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:27:56.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:27:56.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:27:56.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:27:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:27:57.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:27:58.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:27:59.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:27:59.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:28:00.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:28:01.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:28:02.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:28:02.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:28:02.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 03:28:02.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 03:28:02.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:28:02.732 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.86 ms, Average inference time: 8.35 ms

2025-08-01 03:28:02.740 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:28:02.894 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:28:02.973 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch92
2025-08-01 03:28:06.851 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.192s, 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: 1.908e-03, size: 480, ETA: 3:19:38
2025-08-01 03:28:10.536 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.908e-03, size: 544, ETA: 3:19:35
2025-08-01 03:28:14.322 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.907e-03, size: 320, ETA: 3:19:31
2025-08-01 03:28:17.864 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.173s, 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: 1.907e-03, size: 352, ETA: 3:19:27
2025-08-01 03:28:21.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.907e-03, size: 320, ETA: 3:19:22
2025-08-01 03:28:25.341 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.192s, 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: 1.906e-03, size: 576, ETA: 3:19:19
2025-08-01 03:28:27.035 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:28:33.967 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:28:35.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:28:35.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4515
2025-08-01 03:28:36.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4535
2025-08-01 03:28:36.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2006
2025-08-01 03:28:36.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3685
2025-08-01 03:28:36.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:28:36.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:28:36.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 03:28:36.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 03:28:36.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-08-01 03:28:36.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-08-01 03:28:36.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:28:36.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:28:36.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:28:36.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:28:36.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:28:36.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:28:36.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:28:36.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:28:36.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:28:37.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:28:38.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:28:39.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:28:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:28:41.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:28:41.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:28:42.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:28:43.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:28:44.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:28:44.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 03:28:44.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:28:44.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:28:44.872 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.85 ms, Average inference time: 8.20 ms

2025-08-01 03:28:44.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:28:44.973 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:28:45.092 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch93
2025-08-01 03:28:48.826 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.185s, 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: 1.906e-03, size: 416, ETA: 3:19:15
2025-08-01 03:28:52.586 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.905e-03, size: 288, ETA: 3:19:11
2025-08-01 03:28:56.136 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.905e-03, size: 288, ETA: 3:19:06
2025-08-01 03:28:59.922 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.187s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.905e-03, size: 544, ETA: 3:19:03
2025-08-01 03:29:03.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.904e-03, size: 480, ETA: 3:19:00
2025-08-01 03:29:07.505 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.904e-03, size: 384, ETA: 3:18:57
2025-08-01 03:29:09.101 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:29:15.859 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:29:17.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:29:18.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4507
2025-08-01 03:29:18.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3718
2025-08-01 03:29:18.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1993
2025-08-01 03:29:18.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3406
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.199
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.341
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:29:18.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:29:18.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:29:18.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:29:18.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:29:18.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:29:18.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:29:20.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:29:21.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:29:22.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:29:24.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:29:25.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:29:26.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:29:28.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:29:29.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:29:30.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:29:30.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:29:30.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 03:29:30.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:29:30.677 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.84 ms, Average inference time: 8.38 ms

2025-08-01 03:29:30.678 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:29:30.797 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:29:30.887 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch94
2025-08-01 03:29:34.382 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.3, lr: 1.904e-03, size: 480, ETA: 3:18:50
2025-08-01 03:29:38.183 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.903e-03, size: 352, ETA: 3:18:47
2025-08-01 03:29:41.836 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.903e-03, size: 544, ETA: 3:18:43
2025-08-01 03:29:45.568 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, data_time: 0.005s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.903e-03, size: 288, ETA: 3:18:39
2025-08-01 03:29:49.363 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.902e-03, size: 544, ETA: 3:18:36
2025-08-01 03:29:53.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, 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: 1.902e-03, size: 480, ETA: 3:18:33
2025-08-01 03:29:54.759 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:30:01.452 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:30:04.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:30:06.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4578
2025-08-01 03:30:07.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4115
2025-08-01 03:30:07.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2212
2025-08-01 03:30:07.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3635
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.364
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:30:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:30:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:30:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:30:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:30:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:30:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:30:09.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:30:12.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:30:15.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:30:17.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:30:20.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:30:23.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:30:25.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:30:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:30:30.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:30:30.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 03:30:30.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:30:30.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:30:31.025 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.87 ms, Average inference time: 8.41 ms

2025-08-01 03:30:31.027 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:30:31.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:30:31.251 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch95
2025-08-01 03:30:34.836 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.176s, 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: 1.901e-03, size: 288, ETA: 3:18:27
2025-08-01 03:30:38.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.179s, 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: 1.901e-03, size: 544, ETA: 3:18:23
2025-08-01 03:30:42.321 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.6Gb, iter_time: 0.188s, 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: 1.901e-03, size: 384, ETA: 3:18:20
2025-08-01 03:30:46.185 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.190s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.900e-03, size: 544, ETA: 3:18:17
2025-08-01 03:30:49.878 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.900e-03, size: 320, ETA: 3:18:13
2025-08-01 03:30:53.657 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.899e-03, size: 320, ETA: 3:18:10
2025-08-01 03:30:55.396 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:31:02.161 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:31:03.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:31:03.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5173
2025-08-01 03:31:03.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4024
2025-08-01 03:31:03.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3264
2025-08-01 03:31:03.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4154
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.326
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.415
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:31:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:31:03.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:31:03.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:31:03.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:31:04.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:31:05.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:31:06.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:31:07.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:31:07.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:31:08.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:31:09.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:31:10.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:31:10.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:31:10.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 03:31:10.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 03:31:10.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:31:11.006 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.85 ms, Average inference time: 8.31 ms

2025-08-01 03:31:11.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:31:11.082 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:31:11.164 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch96
2025-08-01 03:31:14.843 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.182s, 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: 1.899e-03, size: 320, ETA: 3:18:05
2025-08-01 03:31:18.700 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.191s, 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: 1.899e-03, size: 288, ETA: 3:18:02
2025-08-01 03:31:22.406 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, 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: 1.898e-03, size: 512, ETA: 3:17:59
2025-08-01 03:31:26.339 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.192s, data_time: 0.004s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.898e-03, size: 512, ETA: 3:17:56
2025-08-01 03:31:30.052 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.897e-03, size: 288, ETA: 3:17:52
2025-08-01 03:31:33.895 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.897e-03, size: 256, ETA: 3:17:49
2025-08-01 03:31:35.435 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:31:42.278 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:31:45.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:31:47.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2869
2025-08-01 03:31:47.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2935
2025-08-01 03:31:47.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0804
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2203
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.080
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.220
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:31:47.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:31:47.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:31:47.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:31:47.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:31:47.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:31:47.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:31:47.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:31:47.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:31:50.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:31:52.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:31:55.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:31:57.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:32:00.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:32:02.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:32:05.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:32:07.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:32:10.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:32:10.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 03:32:10.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-08-01 03:32:10.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:32:10.455 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.88 ms, Average inference time: 8.36 ms

2025-08-01 03:32:10.458 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:32:10.546 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:32:10.650 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch97
2025-08-01 03:32:14.133 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.897e-03, size: 256, ETA: 3:17:42
2025-08-01 03:32:17.803 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.896e-03, size: 544, ETA: 3:17:38
2025-08-01 03:32:21.585 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.896e-03, size: 512, ETA: 3:17:35
2025-08-01 03:32:25.322 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.896e-03, size: 288, ETA: 3:17:31
2025-08-01 03:32:28.851 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.174s, 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: 1.895e-03, size: 544, ETA: 3:17:26
2025-08-01 03:32:32.528 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.895e-03, size: 480, ETA: 3:17:23
2025-08-01 03:32:34.237 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:32:41.159 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:32:42.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:32:43.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3966
2025-08-01 03:32:44.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3232
2025-08-01 03:32:44.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1491
2025-08-01 03:32:44.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2896
2025-08-01 03:32:44.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.149
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.290
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:32:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:32:44.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:32:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:32:46.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:32:47.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:32:48.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:32:48.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:32:49.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:32:50.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:32:51.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:32:52.897 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:32:52.897 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 03:32:52.897 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 03:32:52.897 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:32:52.905 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.85 ms, Average inference time: 8.26 ms

2025-08-01 03:32:52.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:32:52.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:32:53.067 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch98
2025-08-01 03:32:56.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, 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: 1.894e-03, size: 576, ETA: 3:17:18
2025-08-01 03:33:00.789 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.196s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.894e-03, size: 576, ETA: 3:17:16
2025-08-01 03:33:04.687 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.191s, 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: 1.893e-03, size: 384, ETA: 3:17:13
2025-08-01 03:33:08.329 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 6.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.893e-03, size: 256, ETA: 3:17:09
2025-08-01 03:33:12.139 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.187s, 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: 1.893e-03, size: 544, ETA: 3:17:06
2025-08-01 03:33:15.872 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.892e-03, size: 288, ETA: 3:17:02
2025-08-01 03:33:17.517 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:33:24.259 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:33:25.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:33:25.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3481
2025-08-01 03:33:25.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1335
2025-08-01 03:33:25.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1258
2025-08-01 03:33:25.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2025
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.133
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.126
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.202
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:33:25.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:33:25.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:33:25.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:33:25.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:33:25.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:33:25.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:33:25.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:33:26.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:33:26.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:33:27.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:33:27.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:33:28.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:33:28.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:33:29.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:33:29.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:33:30.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:33:30.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.06
2025-08-01 03:33:30.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.20
2025-08-01 03:33:30.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:33:30.126 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.86 ms, Average inference time: 8.28 ms

2025-08-01 03:33:30.127 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:33:30.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:33:30.288 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch99
2025-08-01 03:33:33.927 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.892e-03, size: 288, ETA: 3:16:56
2025-08-01 03:33:37.514 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.891e-03, size: 320, ETA: 3:16:52
2025-08-01 03:33:41.140 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.891e-03, size: 352, ETA: 3:16:48
2025-08-01 03:33:44.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.891e-03, size: 320, ETA: 3:16:44
2025-08-01 03:33:48.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.890e-03, size: 352, ETA: 3:16:40
2025-08-01 03:33:52.268 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.182s, 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: 1.890e-03, size: 576, ETA: 3:16:36
2025-08-01 03:33:54.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:34:01.026 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:34:02.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:34:03.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4710
2025-08-01 03:34:03.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3781
2025-08-01 03:34:03.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1896
2025-08-01 03:34:03.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3462
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.190
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.346
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:34:03.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:34:03.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:34:03.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:34:03.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:34:03.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:34:03.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:34:03.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:34:04.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:34:05.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:34:07.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:34:08.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:34:10.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:34:11.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:34:12.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:34:13.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:34:15.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:34:15.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 03:34:15.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 03:34:15.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:34:15.166 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 03:34:15.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:34:15.307 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:34:15.413 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch100
2025-08-01 03:34:18.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.889e-03, size: 352, ETA: 3:16:31
2025-08-01 03:34:22.780 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.190s, 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: 1.889e-03, size: 576, ETA: 3:16:28
2025-08-01 03:34:26.736 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.197s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.889e-03, size: 480, ETA: 3:16:26
2025-08-01 03:34:30.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.888e-03, size: 448, ETA: 3:16:22
2025-08-01 03:34:34.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.888e-03, size: 416, ETA: 3:16:19
2025-08-01 03:34:37.781 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.888e-03, size: 480, ETA: 3:16:15
2025-08-01 03:34:39.407 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:34:46.469 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:34:49.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:34:50.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4574
2025-08-01 03:34:51.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3800
2025-08-01 03:34:51.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1984
2025-08-01 03:34:51.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3453
2025-08-01 03:34:51.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:34:51.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:34:51.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:34:51.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:34:51.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:34:53.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:34:55.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:34:57.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:34:59.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:35:02.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:35:04.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:35:06.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:35:08.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:35:10.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:35:10.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:35:10.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 03:35:10.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:35:10.847 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.86 ms, Average inference time: 8.31 ms

2025-08-01 03:35:10.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:35:10.927 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:35:11.011 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch101
2025-08-01 03:35:14.670 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.887e-03, size: 320, ETA: 3:16:09
2025-08-01 03:35:18.308 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.887e-03, size: 256, ETA: 3:16:05
2025-08-01 03:35:22.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.186s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.886e-03, size: 416, ETA: 3:16:02
2025-08-01 03:35:25.978 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.886e-03, size: 352, ETA: 3:15:59
2025-08-01 03:35:29.647 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.885e-03, size: 384, ETA: 3:15:55
2025-08-01 03:35:33.271 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.177s, 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: 1.885e-03, size: 448, ETA: 3:15:51
2025-08-01 03:35:34.937 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:35:41.607 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:35:43.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:35:43.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4633
2025-08-01 03:35:44.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3992
2025-08-01 03:35:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2423
2025-08-01 03:35:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3683
2025-08-01 03:35:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:35:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:35:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-01 03:35:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-01 03:35:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 03:35:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.368
2025-08-01 03:35:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:35:44.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:35:44.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:35:44.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:35:44.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:35:44.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:35:44.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:35:44.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:35:44.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:35:45.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:35:46.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:35:47.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:35:48.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:35:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:35:51.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:35:52.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:35:53.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:35:54.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:35:54.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 03:35:54.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:35:54.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:35:54.735 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 03:35:54.737 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:35:54.861 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:35:54.947 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch102
2025-08-01 03:35:58.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.884e-03, size: 480, ETA: 3:15:45
2025-08-01 03:36:02.238 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, 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: 1.884e-03, size: 256, ETA: 3:15:42
2025-08-01 03:36:06.202 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.194s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.884e-03, size: 256, ETA: 3:15:39
2025-08-01 03:36:10.054 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.883e-03, size: 320, ETA: 3:15:36
2025-08-01 03:36:13.861 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.883e-03, size: 576, ETA: 3:15:33
2025-08-01 03:36:17.683 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.189s, 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: 1.883e-03, size: 416, ETA: 3:15:30
2025-08-01 03:36:19.501 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:36:26.193 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:36:27.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:36:27.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3687
2025-08-01 03:36:27.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3098
2025-08-01 03:36:27.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1600
2025-08-01 03:36:27.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2795
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.160
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.279
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:36:27.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:36:27.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:36:27.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:36:27.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:36:27.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:36:28.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:36:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:36:30.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:36:30.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:36:31.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:36:32.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:36:32.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:36:33.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:36:34.425 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:36:34.425 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-08-01 03:36:34.425 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 03:36:34.425 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:36:34.432 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.81 ms, Average inference time: 8.22 ms

2025-08-01 03:36:34.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:36:34.514 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:36:34.598 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch103
2025-08-01 03:36:38.320 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.882e-03, size: 576, ETA: 3:15:25
2025-08-01 03:36:42.049 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, 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: 1.882e-03, size: 288, ETA: 3:15:22
2025-08-01 03:36:45.800 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.881e-03, size: 448, ETA: 3:15:18
2025-08-01 03:36:49.579 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, 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: 1.881e-03, size: 448, ETA: 3:15:14
2025-08-01 03:36:53.237 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.179s, 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: 1.880e-03, size: 480, ETA: 3:15:10
2025-08-01 03:36:57.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, 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: 1.880e-03, size: 384, ETA: 3:15:07
2025-08-01 03:36:58.671 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:37:05.617 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:37:09.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:37:12.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3827
2025-08-01 03:37:12.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3569
2025-08-01 03:37:12.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2270
2025-08-01 03:37:12.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3222
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:37:12.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:37:12.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:37:12.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:37:12.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:37:12.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:37:16.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:37:19.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:37:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:37:26.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:37:29.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:37:33.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:37:37.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:37:40.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:37:44.176 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:37:44.176 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:37:44.176 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 03:37:44.176 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:37:44.205 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.87 ms, Average inference time: 8.40 ms

2025-08-01 03:37:44.206 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:37:44.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:37:44.368 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch104
2025-08-01 03:37:47.955 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.879e-03, size: 352, ETA: 3:15:01
2025-08-01 03:37:52.006 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.200s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.879e-03, size: 480, ETA: 3:14:59
2025-08-01 03:37:55.664 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.879e-03, size: 288, ETA: 3:14:55
2025-08-01 03:37:59.427 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.182s, 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: 1.878e-03, size: 448, ETA: 3:14:51
2025-08-01 03:38:03.156 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.878e-03, size: 288, ETA: 3:14:48
2025-08-01 03:38:06.828 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.878e-03, size: 416, ETA: 3:14:44
2025-08-01 03:38:08.498 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:38:15.413 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:38:18.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:38:20.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4660
2025-08-01 03:38:20.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4318
2025-08-01 03:38:20.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1793
2025-08-01 03:38:20.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3590
2025-08-01 03:38:20.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:38:20.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:38:20.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-01 03:38:20.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 03:38:20.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.179
2025-08-01 03:38:20.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:38:20.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:38:23.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:38:25.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:38:28.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:38:30.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:38:33.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:38:35.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:38:38.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:38:40.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:38:43.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:38:43.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:38:43.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:38:43.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:38:43.051 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.91 ms, Average inference time: 8.38 ms

2025-08-01 03:38:43.053 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:38:43.138 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:38:43.246 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch105
2025-08-01 03:38:47.051 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.877e-03, size: 416, ETA: 3:14:39
2025-08-01 03:38:50.714 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.877e-03, size: 288, ETA: 3:14:35
2025-08-01 03:38:54.405 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.181s, 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: 1.876e-03, size: 320, ETA: 3:14:31
2025-08-01 03:38:58.175 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.876e-03, size: 480, ETA: 3:14:28
2025-08-01 03:39:01.832 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.178s, 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: 1.875e-03, size: 384, ETA: 3:14:24
2025-08-01 03:39:05.585 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.182s, 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: 1.875e-03, size: 448, ETA: 3:14:20
2025-08-01 03:39:07.318 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:39:14.172 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:39:16.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:39:18.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4486
2025-08-01 03:39:18.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3576
2025-08-01 03:39:18.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1917
2025-08-01 03:39:18.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3326
2025-08-01 03:39:18.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:39:18.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:39:18.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-01 03:39:18.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-01 03:39:18.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.192
2025-08-01 03:39:18.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.333
2025-08-01 03:39:18.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:39:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:39:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:39:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:39:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:39:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:39:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:39:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:39:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:39:20.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:39:22.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:39:24.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:39:25.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:39:27.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:39:29.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:39:31.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:39:33.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:39:35.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:39:35.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 03:39:35.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 03:39:35.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:39:35.196 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.88 ms, Average inference time: 8.35 ms

2025-08-01 03:39:35.197 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:39:35.309 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:39:35.447 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch106
2025-08-01 03:39:39.215 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.874e-03, size: 544, ETA: 3:14:15
2025-08-01 03:39:42.870 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.874e-03, size: 448, ETA: 3:14:11
2025-08-01 03:39:46.520 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.874e-03, size: 448, ETA: 3:14:08
2025-08-01 03:39:50.310 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.6, lr: 1.873e-03, size: 320, ETA: 3:14:04
2025-08-01 03:39:54.269 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.873e-03, size: 352, ETA: 3:14:01
2025-08-01 03:39:58.078 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, 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: 1.872e-03, size: 256, ETA: 3:13:58
2025-08-01 03:39:59.673 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:40:06.442 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:40:08.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:40:09.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1843
2025-08-01 03:40:10.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1740
2025-08-01 03:40:10.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0909
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1497
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.174
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.091
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.150
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:40:10.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:40:10.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:40:10.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:40:10.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:40:10.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:40:10.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:40:11.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:40:13.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:40:14.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:40:16.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:40:18.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:40:19.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:40:21.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:40:22.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:40:24.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:40:24.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.06
2025-08-01 03:40:24.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.15
2025-08-01 03:40:24.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:40:24.431 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.88 ms, Average inference time: 8.25 ms

2025-08-01 03:40:24.432 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:40:24.509 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:40:24.596 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch107
2025-08-01 03:40:28.267 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, 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: 1.872e-03, size: 288, ETA: 3:13:52
2025-08-01 03:40:31.907 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.871e-03, size: 544, ETA: 3:13:48
2025-08-01 03:40:35.702 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, 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: 1.871e-03, size: 544, ETA: 3:13:45
2025-08-01 03:40:39.442 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.871e-03, size: 352, ETA: 3:13:41
2025-08-01 03:40:43.049 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.178s, 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: 1.870e-03, size: 288, ETA: 3:13:37
2025-08-01 03:40:46.860 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.870e-03, size: 288, ETA: 3:13:34
2025-08-01 03:40:48.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:40:55.368 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:41:03.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:41:07.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1723
2025-08-01 03:41:08.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1576
2025-08-01 03:41:08.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1187
2025-08-01 03:41:08.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1495
2025-08-01 03:41:08.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:41:08.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:41:08.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.172
2025-08-01 03:41:08.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.158
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.119
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.150
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:41:08.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:41:13.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:41:18.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:41:23.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:41:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:41:33.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:41:37.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:41:42.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:41:47.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:41:52.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:41:52.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-08-01 03:41:52.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.15
2025-08-01 03:41:52.715 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:41:52.741 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.87 ms, Average inference time: 8.27 ms

2025-08-01 03:41:52.742 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:41:52.814 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:41:52.896 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch108
2025-08-01 03:41:56.442 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.869e-03, size: 512, ETA: 3:13:27
2025-08-01 03:42:00.414 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.196s, 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: 1.869e-03, size: 448, ETA: 3:13:25
2025-08-01 03:42:04.260 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.868e-03, size: 320, ETA: 3:13:22
2025-08-01 03:42:07.996 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.185s, 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: 1.868e-03, size: 512, ETA: 3:13:18
2025-08-01 03:42:11.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.868e-03, size: 320, ETA: 3:13:16
2025-08-01 03:42:15.582 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.867e-03, size: 512, ETA: 3:13:12
2025-08-01 03:42:17.238 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:42:24.222 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:42:29.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:42:34.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4556
2025-08-01 03:42:35.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4033
2025-08-01 03:42:35.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2210
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3600
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.360
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:42:35.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:42:35.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:42:35.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:42:35.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:42:35.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:42:35.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:42:40.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:42:45.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:42:50.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:42:55.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:43:00.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:43:05.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:43:11.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:43:16.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:43:21.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:43:21.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 03:43:21.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:43:21.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:43:21.284 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.86 ms, Average inference time: 8.34 ms

2025-08-01 03:43:21.285 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:43:21.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:43:21.458 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch109
2025-08-01 03:43:25.052 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.177s, 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: 1.866e-03, size: 416, ETA: 3:13:06
2025-08-01 03:43:28.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.866e-03, size: 448, ETA: 3:13:03
2025-08-01 03:43:32.642 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.866e-03, size: 416, ETA: 3:12:59
2025-08-01 03:43:36.392 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.183s, 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: 1.865e-03, size: 416, ETA: 3:12:56
2025-08-01 03:43:40.026 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.865e-03, size: 544, ETA: 3:12:52
2025-08-01 03:43:43.869 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.864e-03, size: 320, ETA: 3:12:49
2025-08-01 03:43:45.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:43:52.211 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:43:53.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:43:53.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4275
2025-08-01 03:43:53.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3445
2025-08-01 03:43:53.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2487
2025-08-01 03:43:53.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3402
2025-08-01 03:43:53.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:43:53.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:43:53.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-08-01 03:43:53.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-08-01 03:43:53.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 03:43:53.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.340
2025-08-01 03:43:53.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:43:53.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:43:53.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:43:53.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:43:53.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:43:53.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:43:53.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:43:53.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:43:53.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:43:54.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:43:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:43:56.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:43:56.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:43:57.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:43:58.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:43:58.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:43:59.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:44:00.061 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:44:00.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 03:44:00.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 03:44:00.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:44:00.070 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.83 ms, Average inference time: 8.22 ms

2025-08-01 03:44:00.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:44:00.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:44:00.248 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch110
2025-08-01 03:44:03.933 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.864e-03, size: 512, ETA: 3:12:43
2025-08-01 03:44:07.588 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, 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: 1.863e-03, size: 384, ETA: 3:12:39
2025-08-01 03:44:11.440 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.863e-03, size: 480, ETA: 3:12:36
2025-08-01 03:44:15.094 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.179s, 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: 1.863e-03, size: 352, ETA: 3:12:32
2025-08-01 03:44:18.678 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.862e-03, size: 512, ETA: 3:12:28
2025-08-01 03:44:22.344 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, 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: 1.862e-03, size: 320, ETA: 3:12:24
2025-08-01 03:44:23.962 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:44:30.889 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:44:36.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:44:39.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4111
2025-08-01 03:44:40.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3940
2025-08-01 03:44:40.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1857
2025-08-01 03:44:40.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3303
2025-08-01 03:44:40.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.330
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:44:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:44:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:44:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:44:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:44:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:44:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:44:45.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:44:50.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:44:54.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:44:59.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:45:04.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:45:09.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:45:13.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:45:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:45:22.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:45:22.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:45:22.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 03:45:22.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:45:22.974 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.87 ms, Average inference time: 8.33 ms

2025-08-01 03:45:22.975 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:45:23.096 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:45:23.183 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch111
2025-08-01 03:45:26.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.861e-03, size: 512, ETA: 3:12:18
2025-08-01 03:45:30.526 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.861e-03, size: 512, ETA: 3:12:15
2025-08-01 03:45:34.253 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.860e-03, size: 512, ETA: 3:12:11
2025-08-01 03:45:38.018 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.860e-03, size: 384, ETA: 3:12:08
2025-08-01 03:45:41.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.180s, 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: 1.859e-03, size: 448, ETA: 3:12:04
2025-08-01 03:45:45.367 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.7Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.859e-03, size: 512, ETA: 3:12:01
2025-08-01 03:45:46.979 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:45:53.721 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:45:55.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:45:56.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4667
2025-08-01 03:45:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4116
2025-08-01 03:45:56.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2319
2025-08-01 03:45:56.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3701
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:45:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:45:56.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:45:56.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:45:56.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:45:56.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:45:56.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:45:58.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:45:59.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:46:00.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:46:02.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:46:03.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:46:04.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:46:06.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:46:07.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:46:08.985 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:46:08.985 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 03:46:08.986 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:46:08.986 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:46:08.994 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.85 ms, Average inference time: 8.35 ms

2025-08-01 03:46:08.995 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:46:09.111 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:46:09.198 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch112
2025-08-01 03:46:12.733 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.172s, 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: 1.858e-03, size: 256, ETA: 3:11:54
2025-08-01 03:46:16.434 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.858e-03, size: 352, ETA: 3:11:50
2025-08-01 03:46:20.243 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.183s, data_time: 0.005s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.858e-03, size: 448, ETA: 3:11:47
2025-08-01 03:46:23.937 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.182s, 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: 1.857e-03, size: 448, ETA: 3:11:43
2025-08-01 03:46:27.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, 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: 1.857e-03, size: 480, ETA: 3:11:39
2025-08-01 03:46:31.488 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.8, cls_loss: 1.2, lr: 1.856e-03, size: 544, ETA: 3:11:36
2025-08-01 03:46:33.236 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:46:40.119 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:46:43.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:46:45.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4678
2025-08-01 03:46:45.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2820
2025-08-01 03:46:45.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2020
2025-08-01 03:46:45.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3173
2025-08-01 03:46:45.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:46:45.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:46:45.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-01 03:46:45.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.282
2025-08-01 03:46:45.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-08-01 03:46:45.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.317
2025-08-01 03:46:45.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:46:45.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:46:45.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:46:45.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:46:45.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:46:45.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:46:45.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:46:45.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:46:45.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:46:48.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:46:52.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:46:55.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:46:57.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:47:00.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:47:02.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:47:05.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:47:08.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:47:10.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:47:10.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 03:47:10.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 03:47:10.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:47:10.590 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.86 ms, Average inference time: 8.27 ms

2025-08-01 03:47:10.591 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:47:10.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:47:10.784 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch113
2025-08-01 03:47:14.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.175s, 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: 1.856e-03, size: 256, ETA: 3:11:30
2025-08-01 03:47:17.879 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.5, lr: 1.855e-03, size: 448, ETA: 3:11:26
2025-08-01 03:47:21.478 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.855e-03, size: 288, ETA: 3:11:22
2025-08-01 03:47:25.377 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.854e-03, size: 512, ETA: 3:11:19
2025-08-01 03:47:29.088 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.182s, 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: 1.854e-03, size: 416, ETA: 3:11:15
2025-08-01 03:47:32.752 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.178s, 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: 1.854e-03, size: 416, ETA: 3:11:11
2025-08-01 03:47:34.384 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:47:41.342 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:47:43.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:47:44.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3822
2025-08-01 03:47:44.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3418
2025-08-01 03:47:44.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1354
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2865
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.135
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.286
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:47:44.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:47:44.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:47:44.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:47:44.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:47:44.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:47:44.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:47:44.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:47:45.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:47:47.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:47:48.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:47:50.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:47:51.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:47:52.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:47:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:47:55.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:47:56.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:47:56.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 03:47:56.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 03:47:56.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:47:56.860 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 03:47:56.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:47:56.938 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:47:57.044 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch114
2025-08-01 03:48:00.687 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.853e-03, size: 448, ETA: 3:11:06
2025-08-01 03:48:04.621 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.191s, 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: 1.852e-03, size: 576, ETA: 3:11:03
2025-08-01 03:48:08.449 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.190s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.852e-03, size: 544, ETA: 3:11:00
2025-08-01 03:48:12.122 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.852e-03, size: 384, ETA: 3:10:56
2025-08-01 03:48:16.075 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.194s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.851e-03, size: 576, ETA: 3:10:53
2025-08-01 03:48:19.868 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.185s, 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: 1.851e-03, size: 288, ETA: 3:10:50
2025-08-01 03:48:21.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:48:28.276 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:48:32.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:48:36.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3139
2025-08-01 03:48:36.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3393
2025-08-01 03:48:36.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1186
2025-08-01 03:48:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2572
2025-08-01 03:48:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:48:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.119
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.257
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:48:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:48:36.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:48:36.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:48:36.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:48:40.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:48:44.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:48:48.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:48:52.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:48:56.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:49:00.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:49:04.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:49:08.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:49:12.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:49:12.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 03:49:12.018 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 03:49:12.018 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:49:12.043 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.88 ms, Average inference time: 8.36 ms

2025-08-01 03:49:12.045 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:49:12.117 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:49:12.200 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch115
2025-08-01 03:49:15.900 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.183s, data_time: 0.004s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.850e-03, size: 384, ETA: 3:10:44
2025-08-01 03:49:19.731 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, 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: 1.850e-03, size: 416, ETA: 3:10:41
2025-08-01 03:49:23.495 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.849e-03, size: 448, ETA: 3:10:37
2025-08-01 03:49:27.056 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.849e-03, size: 256, ETA: 3:10:33
2025-08-01 03:49:30.783 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.848e-03, size: 448, ETA: 3:10:30
2025-08-01 03:49:34.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.186s, 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: 1.848e-03, size: 288, ETA: 3:10:26
2025-08-01 03:49:36.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:49:43.028 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:49:46.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:49:50.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3972
2025-08-01 03:49:50.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3430
2025-08-01 03:49:50.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1484
2025-08-01 03:49:50.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2962
2025-08-01 03:49:50.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:49:50.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:49:50.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.148
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.296
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:49:50.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:49:50.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:49:50.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:49:54.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:49:57.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:50:01.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:50:05.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:50:08.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:50:11.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:50:15.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:50:18.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:50:21.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:50:21.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:50:21.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 03:50:21.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:50:21.836 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.89 ms, Average inference time: 8.37 ms

2025-08-01 03:50:21.838 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:50:21.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:50:22.008 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch116
2025-08-01 03:50:25.559 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.847e-03, size: 576, ETA: 3:10:20
2025-08-01 03:50:29.413 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.847e-03, size: 320, ETA: 3:10:17
2025-08-01 03:50:32.991 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.846e-03, size: 256, ETA: 3:10:13
2025-08-01 03:50:36.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.178s, 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: 1.846e-03, size: 544, ETA: 3:10:09
2025-08-01 03:50:40.491 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.188s, 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: 1.846e-03, size: 544, ETA: 3:10:06
2025-08-01 03:50:44.222 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.845e-03, size: 352, ETA: 3:10:02
2025-08-01 03:50:45.939 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:50:52.887 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:50:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:50:54.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4557
2025-08-01 03:50:55.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3885
2025-08-01 03:50:55.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2666
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3703
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:50:55.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:50:55.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:50:55.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:50:55.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:50:55.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:50:55.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:50:55.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:50:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:50:57.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:50:57.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:50:58.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:50:59.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:51:00.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:51:01.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:51:02.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:51:03.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:51:03.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:51:03.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:51:03.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:51:03.579 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.87 ms, Average inference time: 8.32 ms

2025-08-01 03:51:03.581 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:51:03.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:51:03.766 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch117
2025-08-01 03:51:07.262 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.844e-03, size: 544, ETA: 3:09:56
2025-08-01 03:51:11.036 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.184s, 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: 1.844e-03, size: 512, ETA: 3:09:52
2025-08-01 03:51:14.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.3, lr: 1.844e-03, size: 480, ETA: 3:09:49
2025-08-01 03:51:18.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.843e-03, size: 576, ETA: 3:09:45
2025-08-01 03:51:22.300 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.189s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.843e-03, size: 544, ETA: 3:09:42
2025-08-01 03:51:26.113 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.842e-03, size: 544, ETA: 3:09:39
2025-08-01 03:51:27.868 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:51:34.616 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:51:36.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:51:37.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4917
2025-08-01 03:51:37.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4003
2025-08-01 03:51:37.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2515
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3812
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.251
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.381
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:51:37.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:51:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:51:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:51:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:51:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:51:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:51:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:51:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:51:38.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:51:40.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:51:41.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:51:42.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:51:43.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:51:45.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:51:46.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:51:47.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:51:48.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:51:48.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 03:51:48.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 03:51:48.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:51:48.894 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.84 ms, Average inference time: 8.33 ms

2025-08-01 03:51:48.895 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:51:48.968 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:51:49.053 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch118
2025-08-01 03:51:52.694 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.842e-03, size: 352, ETA: 3:09:33
2025-08-01 03:51:56.525 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 1.6, cls_loss: 0.7, lr: 1.841e-03, size: 416, ETA: 3:09:30
2025-08-01 03:52:00.359 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.841e-03, size: 416, ETA: 3:09:27
2025-08-01 03:52:04.074 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.840e-03, size: 512, ETA: 3:09:23
2025-08-01 03:52:07.794 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.9, lr: 1.840e-03, size: 352, ETA: 3:09:19
2025-08-01 03:52:11.590 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.839e-03, size: 544, ETA: 3:09:16
2025-08-01 03:52:13.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:52:20.218 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:52:26.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:52:31.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4007
2025-08-01 03:52:31.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2647
2025-08-01 03:52:31.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1691
2025-08-01 03:52:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2781
2025-08-01 03:52:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:52:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:52:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-08-01 03:52:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.265
2025-08-01 03:52:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-08-01 03:52:31.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.278
2025-08-01 03:52:31.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:52:31.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:52:31.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:52:31.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:52:31.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:52:31.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:52:31.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:52:31.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:52:31.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:52:37.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:52:42.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:52:47.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:52:53.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:52:59.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:53:06.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:53:11.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:53:17.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:53:22.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:53:22.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 03:53:22.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 03:53:22.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:53:22.520 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.88 ms, Average inference time: 8.48 ms

2025-08-01 03:53:22.523 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:53:22.595 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:53:22.677 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch119
2025-08-01 03:53:26.459 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.839e-03, size: 576, ETA: 3:09:12
2025-08-01 03:53:30.470 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.197s, 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: 1.838e-03, size: 480, ETA: 3:09:09
2025-08-01 03:53:34.295 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 5.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.838e-03, size: 480, ETA: 3:09:06
2025-08-01 03:53:38.184 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.189s, 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: 1.837e-03, size: 416, ETA: 3:09:03
2025-08-01 03:53:41.899 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.837e-03, size: 576, ETA: 3:08:59
2025-08-01 03:53:45.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.192s, 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: 1.836e-03, size: 480, ETA: 3:08:56
2025-08-01 03:53:47.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:53:54.408 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:53:56.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:53:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4835
2025-08-01 03:53:57.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4172
2025-08-01 03:53:57.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1979
2025-08-01 03:53:57.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3662
2025-08-01 03:53:57.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:53:57.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:53:57.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-08-01 03:53:57.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-01 03:53:57.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-08-01 03:53:57.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.366
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:53:57.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:53:59.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:54:00.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:54:01.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:54:03.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:54:04.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:54:05.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:54:07.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:54:08.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:54:09.820 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:54:09.820 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 03:54:09.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 03:54:09.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:54:09.837 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.85 ms, Average inference time: 8.35 ms

2025-08-01 03:54:09.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:54:09.951 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:54:10.067 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch120
2025-08-01 03:54:13.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, 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: 1.836e-03, size: 352, ETA: 3:08:51
2025-08-01 03:54:17.353 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.176s, 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: 1.835e-03, size: 416, ETA: 3:08:47
2025-08-01 03:54:21.125 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.184s, 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: 1.835e-03, size: 480, ETA: 3:08:44
2025-08-01 03:54:25.025 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.0, lr: 1.834e-03, size: 256, ETA: 3:08:41
2025-08-01 03:54:28.755 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.183s, 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: 1.834e-03, size: 576, ETA: 3:08:37
2025-08-01 03:54:32.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.834e-03, size: 416, ETA: 3:08:34
2025-08-01 03:54:34.184 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:54:41.046 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:54:43.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:54:44.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3983
2025-08-01 03:54:45.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3367
2025-08-01 03:54:45.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1921
2025-08-01 03:54:45.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3090
2025-08-01 03:54:45.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:54:45.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:54:45.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.192
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:54:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:54:47.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:54:49.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:54:51.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:54:53.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:54:55.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:54:57.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:54:59.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:55:01.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:55:03.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:55:03.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 03:55:03.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 03:55:03.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:55:03.485 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.87 ms, Average inference time: 8.35 ms

2025-08-01 03:55:03.486 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:55:03.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:55:03.655 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch121
2025-08-01 03:55:07.237 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.178s, 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: 1.833e-03, size: 480, ETA: 3:08:28
2025-08-01 03:55:10.826 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.178s, 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: 1.832e-03, size: 288, ETA: 3:08:24
2025-08-01 03:55:14.896 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.200s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.832e-03, size: 512, ETA: 3:08:22
2025-08-01 03:55:18.563 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.831e-03, size: 320, ETA: 3:08:18
2025-08-01 03:55:22.566 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.199s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.8, lr: 1.831e-03, size: 544, ETA: 3:08:15
2025-08-01 03:55:26.334 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.5, lr: 1.831e-03, size: 320, ETA: 3:08:12
2025-08-01 03:55:27.988 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:55:34.987 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:55:36.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:55:38.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3645
2025-08-01 03:55:38.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3176
2025-08-01 03:55:38.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1979
2025-08-01 03:55:38.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2933
2025-08-01 03:55:38.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:55:38.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:55:38.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.293
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:55:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:55:38.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:55:38.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:55:38.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:55:39.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:55:41.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:55:42.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:55:44.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:55:45.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:55:47.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:55:48.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:55:50.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:55:51.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:55:51.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 03:55:51.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 03:55:51.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:55:51.848 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.86 ms, Average inference time: 8.29 ms

2025-08-01 03:55:51.850 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:55:51.929 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:55:52.022 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch122
2025-08-01 03:55:55.710 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, 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: 1.830e-03, size: 544, ETA: 3:08:07
2025-08-01 03:55:59.521 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.829e-03, size: 512, ETA: 3:08:03
2025-08-01 03:56:03.230 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.181s, data_time: 0.005s, total_loss: 6.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.829e-03, size: 256, ETA: 3:08:00
2025-08-01 03:56:07.066 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.829e-03, size: 512, ETA: 3:07:56
2025-08-01 03:56:10.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.828e-03, size: 448, ETA: 3:07:53
2025-08-01 03:56:14.709 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.191s, 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: 1.828e-03, size: 576, ETA: 3:07:50
2025-08-01 03:56:16.382 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:56:23.189 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:56:27.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:56:30.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4368
2025-08-01 03:56:30.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4452
2025-08-01 03:56:30.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2027
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3615
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.203
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.362
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:56:30.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:56:30.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:56:30.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:56:30.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:56:30.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:56:30.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:56:30.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:56:30.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:56:34.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:56:38.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:56:41.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:56:45.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:56:48.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:56:52.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:56:55.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:56:59.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:57:02.797 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:57:02.797 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 03:57:02.797 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:57:02.797 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:57:02.824 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.87 ms, Average inference time: 8.31 ms

2025-08-01 03:57:02.828 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:57:02.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:57:02.993 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch123
2025-08-01 03:57:06.469 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.827e-03, size: 256, ETA: 3:07:44
2025-08-01 03:57:09.989 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.826e-03, size: 320, ETA: 3:07:39
2025-08-01 03:57:13.808 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.826e-03, size: 576, ETA: 3:07:36
2025-08-01 03:57:17.458 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.826e-03, size: 288, ETA: 3:07:32
2025-08-01 03:57:21.264 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, 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: 1.825e-03, size: 256, ETA: 3:07:29
2025-08-01 03:57:25.043 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, 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: 1.825e-03, size: 544, ETA: 3:07:25
2025-08-01 03:57:26.767 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:57:33.748 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:57:37.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:57:39.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4528
2025-08-01 03:57:39.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4074
2025-08-01 03:57:39.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2152
2025-08-01 03:57:39.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3585
2025-08-01 03:57:39.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:57:39.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:57:39.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-01 03:57:39.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 03:57:39.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.215
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.358
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:57:39.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:57:42.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:57:44.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:57:46.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:57:49.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:57:51.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:57:53.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:57:55.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:57:57.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:57:59.970 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:57:59.970 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 03:57:59.970 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 03:57:59.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:58:00.002 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.87 ms, Average inference time: 8.38 ms

2025-08-01 03:58:00.003 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:58:00.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:58:00.235 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch124
2025-08-01 03:58:03.891 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.824e-03, size: 448, ETA: 3:07:20
2025-08-01 03:58:07.506 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.823e-03, size: 320, ETA: 3:07:16
2025-08-01 03:58:11.313 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.186s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.823e-03, size: 352, ETA: 3:07:12
2025-08-01 03:58:15.036 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.183s, 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: 1.823e-03, size: 352, ETA: 3:07:09
2025-08-01 03:58:18.675 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.822e-03, size: 448, ETA: 3:07:05
2025-08-01 03:58:22.435 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.185s, 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: 1.822e-03, size: 352, ETA: 3:07:01
2025-08-01 03:58:24.142 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:58:30.858 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:58:31.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:58:32.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2900
2025-08-01 03:58:32.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2789
2025-08-01 03:58:32.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1258
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2316
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.279
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.126
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.232
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:58:32.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:58:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:58:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:58:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:58:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:58:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:58:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:58:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:58:33.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:58:34.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:58:34.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:58:35.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 03:58:36.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 03:58:36.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 03:58:37.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 03:58:38.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 03:58:38.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 03:58:38.930 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 03:58:38.930 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-08-01 03:58:38.930 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 03:58:38.937 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.84 ms, Average inference time: 8.28 ms

2025-08-01 03:58:38.938 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:58:39.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:58:39.096 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch125
2025-08-01 03:58:42.722 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.179s, 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: 1.821e-03, size: 576, ETA: 3:06:56
2025-08-01 03:58:46.559 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.187s, 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: 1.820e-03, size: 288, ETA: 3:06:53
2025-08-01 03:58:50.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.194s, data_time: 0.005s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.820e-03, size: 544, ETA: 3:06:50
2025-08-01 03:58:54.481 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.194s, 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: 1.819e-03, size: 320, ETA: 3:06:47
2025-08-01 03:58:58.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.190s, 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: 1.819e-03, size: 576, ETA: 3:06:44
2025-08-01 03:59:02.218 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.189s, 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: 1.819e-03, size: 512, ETA: 3:06:41
2025-08-01 03:59:03.861 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 03:59:10.707 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 03:59:18.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 03:59:25.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4008
2025-08-01 03:59:25.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3124
2025-08-01 03:59:25.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2047
2025-08-01 03:59:25.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3060
2025-08-01 03:59:25.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 03:59:25.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 03:59:25.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-08-01 03:59:25.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-01 03:59:25.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.205
2025-08-01 03:59:25.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.306
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 03:59:25.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 03:59:33.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 03:59:40.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 03:59:47.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 03:59:54.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:00:02.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:00:10.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:00:17.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:00:24.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:00:31.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:00:31.791 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:00:31.791 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 04:00:31.791 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:00:31.823 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.90 ms, Average inference time: 8.41 ms

2025-08-01 04:00:31.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:00:31.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:00:31.991 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch126
2025-08-01 04:00:35.662 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.818e-03, size: 384, ETA: 3:06:35
2025-08-01 04:00:39.258 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.817e-03, size: 416, ETA: 3:06:31
2025-08-01 04:00:42.826 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.175s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.817e-03, size: 256, ETA: 3:06:27
2025-08-01 04:00:46.532 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 1.816e-03, size: 256, ETA: 3:06:23
2025-08-01 04:00:50.350 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.816e-03, size: 288, ETA: 3:06:20
2025-08-01 04:00:54.165 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.187s, 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: 1.815e-03, size: 576, ETA: 3:06:16
2025-08-01 04:00:55.913 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:01:02.663 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:01:04.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:01:05.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3688
2025-08-01 04:01:05.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3559
2025-08-01 04:01:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1802
2025-08-01 04:01:05.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3017
2025-08-01 04:01:05.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.180
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.302
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:01:05.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:01:05.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:01:05.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:01:07.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:01:08.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:01:10.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:01:11.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:01:12.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:01:14.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:01:15.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:01:16.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:01:18.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:01:18.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 04:01:18.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 04:01:18.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:01:18.354 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.84 ms, Average inference time: 8.33 ms

2025-08-01 04:01:18.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:01:18.430 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:01:18.513 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch127
2025-08-01 04:01:22.032 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.172s, 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: 1.815e-03, size: 544, ETA: 3:06:11
2025-08-01 04:01:25.924 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.814e-03, size: 448, ETA: 3:06:08
2025-08-01 04:01:29.765 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.814e-03, size: 288, ETA: 3:06:04
2025-08-01 04:01:33.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.192s, 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: 1.813e-03, size: 256, ETA: 3:06:01
2025-08-01 04:01:37.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.813e-03, size: 256, ETA: 3:05:58
2025-08-01 04:01:41.484 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.186s, 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: 1.812e-03, size: 352, ETA: 3:05:55
2025-08-01 04:01:43.109 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:01:49.904 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:01:53.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:01:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4311
2025-08-01 04:01:56.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2853
2025-08-01 04:01:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1827
2025-08-01 04:01:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2997
2025-08-01 04:01:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:01:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:01:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 04:01:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-08-01 04:01:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.183
2025-08-01 04:01:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.300
2025-08-01 04:01:56.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:01:56.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:01:56.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:01:56.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:01:56.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:01:56.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:01:56.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:01:56.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:01:56.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:01:59.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:02:02.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:02:04.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:02:07.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:02:09.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:02:12.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:02:15.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:02:17.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:02:20.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:02:20.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 04:02:20.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 04:02:20.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:02:20.304 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.86 ms, Average inference time: 8.19 ms

2025-08-01 04:02:20.305 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:02:20.388 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:02:20.496 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch128
2025-08-01 04:02:24.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.812e-03, size: 352, ETA: 3:05:49
2025-08-01 04:02:27.723 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.811e-03, size: 512, ETA: 3:05:45
2025-08-01 04:02:31.469 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.811e-03, size: 256, ETA: 3:05:41
2025-08-01 04:02:34.990 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, 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: 0.9, lr: 1.810e-03, size: 416, ETA: 3:05:37
2025-08-01 04:02:38.683 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.810e-03, size: 352, ETA: 3:05:33
2025-08-01 04:02:42.291 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.809e-03, size: 576, ETA: 3:05:29
2025-08-01 04:02:44.114 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:02:51.000 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:02:52.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:02:52.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3806
2025-08-01 04:02:53.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3945
2025-08-01 04:02:53.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1678
2025-08-01 04:02:53.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3143
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.168
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.314
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:02:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:02:53.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:02:53.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:02:53.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:02:53.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:02:53.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:02:54.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:02:54.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:02:55.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:02:56.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:02:57.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:02:58.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:02:59.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:03:00.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:03:01.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:03:01.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 04:03:01.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 04:03:01.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:03:01.092 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.85 ms, Average inference time: 8.30 ms

2025-08-01 04:03:01.094 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:03:01.175 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:03:01.260 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch129
2025-08-01 04:03:05.005 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, 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: 1.809e-03, size: 384, ETA: 3:05:24
2025-08-01 04:03:08.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.176s, 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: 1.808e-03, size: 352, ETA: 3:05:20
2025-08-01 04:03:12.285 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.808e-03, size: 480, ETA: 3:05:16
2025-08-01 04:03:16.002 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.183s, 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: 1.807e-03, size: 288, ETA: 3:05:12
2025-08-01 04:03:19.580 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.807e-03, size: 544, ETA: 3:05:08
2025-08-01 04:03:23.306 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.184s, 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: 1.806e-03, size: 384, ETA: 3:05:05
2025-08-01 04:03:24.927 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:03:31.624 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:03:32.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:03:33.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3141
2025-08-01 04:03:33.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3124
2025-08-01 04:03:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0902
2025-08-01 04:03:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2389
2025-08-01 04:03:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:03:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:03:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-01 04:03:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-01 04:03:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.090
2025-08-01 04:03:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.239
2025-08-01 04:03:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:03:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:03:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:03:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:03:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:03:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:03:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:03:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:03:33.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:03:34.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:03:34.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:03:35.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:03:36.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:03:37.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:03:37.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:03:38.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:03:39.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:03:40.102 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:03:40.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 04:03:40.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 04:03:40.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:03:40.111 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.86 ms, Average inference time: 8.32 ms

2025-08-01 04:03:40.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:03:40.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:03:40.293 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch130
2025-08-01 04:03:43.801 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.805e-03, size: 448, ETA: 3:04:58
2025-08-01 04:03:47.610 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.805e-03, size: 512, ETA: 3:04:55
2025-08-01 04:03:51.576 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.195s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.2, lr: 1.804e-03, size: 384, ETA: 3:04:52
2025-08-01 04:03:55.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, data_time: 0.001s, 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: 320, ETA: 3:04:49
2025-08-01 04:03:58.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.803e-03, size: 480, ETA: 3:04:44
2025-08-01 04:04:02.708 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.192s, 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: 1.803e-03, size: 288, ETA: 3:04:42
2025-08-01 04:04:04.332 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:04:11.135 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:04:14.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:04:17.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4223
2025-08-01 04:04:17.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3040
2025-08-01 04:04:17.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2015
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3092
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:04:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:04:17.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:04:17.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:04:17.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:04:17.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:04:17.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:04:17.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:04:17.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:04:20.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:04:23.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:04:26.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:04:30.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:04:33.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:04:36.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:04:39.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:04:42.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:04:45.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:04:45.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:04:45.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 04:04:45.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:04:45.426 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.89 ms, Average inference time: 8.42 ms

2025-08-01 04:04:45.428 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:04:45.506 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:04:45.587 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch131
2025-08-01 04:04:49.346 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.802e-03, size: 416, ETA: 3:04:36
2025-08-01 04:04:53.196 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.802e-03, size: 480, ETA: 3:04:33
2025-08-01 04:04:56.826 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.801e-03, size: 384, ETA: 3:04:29
2025-08-01 04:05:00.430 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.8Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.9, lr: 1.801e-03, size: 352, ETA: 3:04:25
2025-08-01 04:05:04.076 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.800e-03, size: 448, ETA: 3:04:21
2025-08-01 04:05:07.908 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.188s, 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: 1.800e-03, size: 448, ETA: 3:04:18
2025-08-01 04:05:09.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:05:16.510 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:05:18.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:05:19.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4310
2025-08-01 04:05:19.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4095
2025-08-01 04:05:19.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1669
2025-08-01 04:05:19.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3358
2025-08-01 04:05:19.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:05:19.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:05:19.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 04:05:19.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-08-01 04:05:19.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.167
2025-08-01 04:05:19.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-08-01 04:05:19.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:05:19.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:05:19.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:05:19.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:05:19.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:05:19.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:05:19.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:05:19.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:05:19.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:05:21.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:05:22.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:05:24.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:05:25.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:05:27.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:05:28.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:05:30.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:05:31.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:05:33.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:05:33.377 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:05:33.377 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 04:05:33.377 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:05:33.390 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.86 ms, Average inference time: 8.35 ms

2025-08-01 04:05:33.392 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:05:33.467 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:05:33.553 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch132
2025-08-01 04:05:37.214 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.799e-03, size: 320, ETA: 3:04:13
2025-08-01 04:05:40.881 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.799e-03, size: 512, ETA: 3:04:09
2025-08-01 04:05:44.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.190s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.798e-03, size: 480, ETA: 3:04:06
2025-08-01 04:05:48.507 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.185s, 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: 1.798e-03, size: 576, ETA: 3:04:02
2025-08-01 04:05:52.469 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.197s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.797e-03, size: 544, ETA: 3:04:00
2025-08-01 04:05:56.247 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.185s, 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: 1.797e-03, size: 512, ETA: 3:03:56
2025-08-01 04:05:57.871 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:06:04.824 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:06:12.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:06:18.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3372
2025-08-01 04:06:19.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3837
2025-08-01 04:06:19.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2061
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3090
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.206
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:06:19.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:06:19.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:06:19.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:06:19.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:06:19.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:06:19.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:06:19.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:06:26.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:06:33.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:06:40.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:06:47.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:06:54.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:07:01.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:07:08.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:07:15.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:07:22.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:07:22.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:07:22.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 04:07:22.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:07:22.274 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.90 ms, Average inference time: 8.41 ms

2025-08-01 04:07:22.276 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:07:22.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:07:22.443 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch133
2025-08-01 04:07:26.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.180s, 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: 1.796e-03, size: 576, ETA: 3:03:50
2025-08-01 04:07:29.994 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.795e-03, size: 544, ETA: 3:03:47
2025-08-01 04:07:33.775 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.8, lr: 1.795e-03, size: 448, ETA: 3:03:44
2025-08-01 04:07:37.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.794e-03, size: 576, ETA: 3:03:40
2025-08-01 04:07:41.439 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.794e-03, size: 320, ETA: 3:03:37
2025-08-01 04:07:45.145 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.793e-03, size: 256, ETA: 3:03:33
2025-08-01 04:07:46.835 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:07:53.679 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:07:54.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:07:55.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4510
2025-08-01 04:07:55.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3988
2025-08-01 04:07:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2543
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3680
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.368
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:07:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:07:55.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:07:55.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:07:55.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:07:55.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:07:55.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:07:56.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:07:57.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:07:58.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:07:59.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:08:00.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:08:01.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:08:02.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:08:03.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:08:04.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:08:04.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 04:08:04.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 04:08:04.658 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:08:04.665 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.88 ms, Average inference time: 8.27 ms

2025-08-01 04:08:04.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:08:04.744 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:08:04.827 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch134
2025-08-01 04:08:08.339 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.793e-03, size: 384, ETA: 3:03:27
2025-08-01 04:08:12.056 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.792e-03, size: 544, ETA: 3:03:24
2025-08-01 04:08:15.964 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.192s, data_time: 0.006s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.792e-03, size: 352, ETA: 3:03:21
2025-08-01 04:08:19.749 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.791e-03, size: 512, ETA: 3:03:17
2025-08-01 04:08:23.619 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.791e-03, size: 576, ETA: 3:03:14
2025-08-01 04:08:27.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, 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: 1.790e-03, size: 544, ETA: 3:03:11
2025-08-01 04:08:29.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:08:36.103 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:08:41.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:08:46.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3762
2025-08-01 04:08:47.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4333
2025-08-01 04:08:47.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1995
2025-08-01 04:08:47.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3363
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.200
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:08:47.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:08:47.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:08:47.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:08:47.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:08:47.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:08:47.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:08:52.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:08:57.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:09:02.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:09:07.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:09:12.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:09:23.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:09:28.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:09:33.255 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:09:33.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 04:09:33.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 04:09:33.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:09:33.282 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.89 ms, Average inference time: 8.39 ms

2025-08-01 04:09:33.283 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:09:33.362 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:09:33.449 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch135
2025-08-01 04:09:37.247 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.184s, 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: 1.789e-03, size: 416, ETA: 3:03:06
2025-08-01 04:09:41.054 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.186s, 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: 1.789e-03, size: 384, ETA: 3:03:03
2025-08-01 04:09:44.985 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, 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: 1.788e-03, size: 256, ETA: 3:03:00
2025-08-01 04:09:48.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.180s, 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: 1.788e-03, size: 448, ETA: 3:02:56
2025-08-01 04:09:52.483 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.787e-03, size: 512, ETA: 3:02:53
2025-08-01 04:09:56.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.179s, 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: 1.787e-03, size: 352, ETA: 3:02:49
2025-08-01 04:09:57.726 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:10:04.449 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:10:06.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:10:07.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2972
2025-08-01 04:10:07.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3355
2025-08-01 04:10:07.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2000
2025-08-01 04:10:07.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2776
2025-08-01 04:10:07.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:10:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:10:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-08-01 04:10:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-08-01 04:10:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.200
2025-08-01 04:10:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.278
2025-08-01 04:10:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:10:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:10:07.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:10:07.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:10:07.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:10:07.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:10:07.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:10:07.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:10:07.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:10:08.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:10:10.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:10:11.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:10:12.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:10:13.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:10:15.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:10:16.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:10:17.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:10:18.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:10:18.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 04:10:18.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 04:10:18.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:10:18.982 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.86 ms, Average inference time: 8.25 ms

2025-08-01 04:10:18.987 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:10:19.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:10:19.147 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch136
2025-08-01 04:10:22.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.173s, 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: 1.786e-03, size: 288, ETA: 3:02:43
2025-08-01 04:10:26.322 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.177s, 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: 1.786e-03, size: 384, ETA: 3:02:38
2025-08-01 04:10:29.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.785e-03, size: 480, ETA: 3:02:35
2025-08-01 04:10:33.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.195s, 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: 1.785e-03, size: 512, ETA: 3:02:32
2025-08-01 04:10:37.744 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.784e-03, size: 256, ETA: 3:02:28
2025-08-01 04:10:41.596 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, 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: 1.783e-03, size: 384, ETA: 3:02:25
2025-08-01 04:10:43.216 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:10:50.056 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:10:52.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:10:54.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3649
2025-08-01 04:10:55.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3608
2025-08-01 04:10:55.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1943
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3066
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.194
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.307
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:10:55.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:10:55.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:10:55.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:10:55.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:10:55.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:10:55.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:10:55.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:10:55.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:10:57.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:11:00.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:11:02.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:11:05.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:11:07.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:11:09.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:11:12.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:11:14.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:11:16.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:11:16.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:11:16.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 04:11:16.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:11:17.009 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.85 ms, Average inference time: 8.30 ms

2025-08-01 04:11:17.012 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:11:17.141 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:11:17.245 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch137
2025-08-01 04:11:20.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.171s, 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: 1.783e-03, size: 256, ETA: 3:02:19
2025-08-01 04:11:24.375 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 1.6, cls_loss: 0.7, lr: 1.782e-03, size: 384, ETA: 3:02:15
2025-08-01 04:11:28.224 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.782e-03, size: 352, ETA: 3:02:12
2025-08-01 04:11:31.876 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.178s, 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: 1.781e-03, size: 416, ETA: 3:02:08
2025-08-01 04:11:35.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.189s, 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: 1.781e-03, size: 256, ETA: 3:02:04
2025-08-01 04:11:39.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.780e-03, size: 512, ETA: 3:02:00
2025-08-01 04:11:40.934 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:11:47.672 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:11:49.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:11:49.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3814
2025-08-01 04:11:49.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3622
2025-08-01 04:11:50.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1752
2025-08-01 04:11:50.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3063
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.175
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.306
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:11:50.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:11:50.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:11:50.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:11:50.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:11:50.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:11:50.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:11:51.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:11:52.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:11:53.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:11:54.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:11:55.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:11:56.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:11:57.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:11:58.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:11:59.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:11:59.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:11:59.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 04:11:59.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:11:59.307 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.86 ms, Average inference time: 8.40 ms

2025-08-01 04:11:59.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:11:59.383 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:11:59.472 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch138
2025-08-01 04:12:03.077 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.175s, 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: 1.779e-03, size: 576, ETA: 3:01:54
2025-08-01 04:12:07.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.196s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.779e-03, size: 416, ETA: 3:01:52
2025-08-01 04:12:10.672 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.778e-03, size: 256, ETA: 3:01:48
2025-08-01 04:12:14.392 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, 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: 1.778e-03, size: 256, ETA: 3:01:44
2025-08-01 04:12:17.944 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.175s, 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: 1.777e-03, size: 480, ETA: 3:01:40
2025-08-01 04:12:21.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, 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: 1.777e-03, size: 288, ETA: 3:01:36
2025-08-01 04:12:23.216 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:12:30.050 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:12:31.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:12:31.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3408
2025-08-01 04:12:31.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3293
2025-08-01 04:12:31.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1635
2025-08-01 04:12:31.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2779
2025-08-01 04:12:31.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:12:31.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:12:31.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-08-01 04:12:31.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.329
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.164
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.278
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:12:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:12:32.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:12:33.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:12:34.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:12:35.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:12:36.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:12:36.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:12:37.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:12:38.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:12:39.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:12:39.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 04:12:39.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 04:12:39.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:12:39.460 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.85 ms, Average inference time: 8.33 ms

2025-08-01 04:12:39.462 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:12:39.542 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:12:39.652 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch139
2025-08-01 04:12:43.376 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, 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: 1.776e-03, size: 448, ETA: 3:01:30
2025-08-01 04:12:47.206 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.776e-03, size: 512, ETA: 3:01:27
2025-08-01 04:12:51.132 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.193s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.775e-03, size: 320, ETA: 3:01:24
2025-08-01 04:12:54.808 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.3, lr: 1.775e-03, size: 448, ETA: 3:01:20
2025-08-01 04:12:58.513 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, 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: 1.774e-03, size: 576, ETA: 3:01:16
2025-08-01 04:13:02.386 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.773e-03, size: 512, ETA: 3:01:13
2025-08-01 04:13:04.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:13:10.970 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:13:13.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:13:15.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4527
2025-08-01 04:13:16.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4061
2025-08-01 04:13:16.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2423
2025-08-01 04:13:16.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3670
2025-08-01 04:13:16.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:13:16.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:13:16.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-01 04:13:16.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.367
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:13:16.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:13:16.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:13:18.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:13:20.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:13:22.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:13:25.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:13:27.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:13:29.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:13:32.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:13:34.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:13:36.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:13:36.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:13:36.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 04:13:36.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:13:36.610 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.87 ms, Average inference time: 8.39 ms

2025-08-01 04:13:36.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:13:36.701 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:13:36.797 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch140
2025-08-01 04:13:40.332 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.773e-03, size: 448, ETA: 3:01:08
2025-08-01 04:13:44.028 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.182s, 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: 1.772e-03, size: 544, ETA: 3:01:04
2025-08-01 04:13:47.908 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, data_time: 0.006s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.772e-03, size: 448, ETA: 3:01:01
2025-08-01 04:13:51.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.176s, 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: 1.771e-03, size: 512, ETA: 3:00:57
2025-08-01 04:13:55.248 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.771e-03, size: 480, ETA: 3:00:53
2025-08-01 04:13:58.948 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, 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: 1.770e-03, size: 480, ETA: 3:00:49
2025-08-01 04:14:00.709 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:14:07.552 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:14:09.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:14:11.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4487
2025-08-01 04:14:11.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4112
2025-08-01 04:14:11.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2225
2025-08-01 04:14:11.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3608
2025-08-01 04:14:11.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:14:11.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:14:11.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:14:11.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:14:11.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:14:11.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:14:11.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:14:13.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:14:14.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:14:16.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:14:17.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:14:19.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:14:20.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:14:22.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:14:24.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:14:25.509 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:14:25.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 04:14:25.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 04:14:25.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:14:25.526 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.87 ms, Average inference time: 8.27 ms

2025-08-01 04:14:25.532 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:14:25.652 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:14:25.749 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch141
2025-08-01 04:14:29.415 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.769e-03, size: 544, ETA: 3:00:44
2025-08-01 04:14:33.057 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.179s, 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: 1.769e-03, size: 320, ETA: 3:00:40
2025-08-01 04:14:36.696 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.768e-03, size: 384, ETA: 3:00:36
2025-08-01 04:14:40.550 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.768e-03, size: 544, ETA: 3:00:33
2025-08-01 04:14:44.484 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.767e-03, size: 576, ETA: 3:00:30
2025-08-01 04:14:48.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, 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: 1.767e-03, size: 384, ETA: 3:00:26
2025-08-01 04:14:49.928 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:14:56.758 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:14:58.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:14:59.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4164
2025-08-01 04:15:00.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4217
2025-08-01 04:15:00.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2317
2025-08-01 04:15:00.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3566
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.357
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:15:00.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:15:00.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:15:00.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:15:00.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:15:00.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:15:01.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:15:03.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:15:04.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:15:05.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:15:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:15:08.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:15:10.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:15:11.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:15:13.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:15:13.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 04:15:13.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 04:15:13.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:15:13.087 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.88 ms, Average inference time: 8.31 ms

2025-08-01 04:15:13.088 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:15:13.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:15:13.314 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch142
2025-08-01 04:15:16.897 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.176s, 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: 1.766e-03, size: 320, ETA: 3:00:21
2025-08-01 04:15:20.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.765e-03, size: 256, ETA: 3:00:17
2025-08-01 04:15:24.268 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.765e-03, size: 512, ETA: 3:00:13
2025-08-01 04:15:28.142 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.8, lr: 1.764e-03, size: 576, ETA: 3:00:10
2025-08-01 04:15:32.003 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.764e-03, size: 544, ETA: 3:00:07
2025-08-01 04:15:35.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.763e-03, size: 256, ETA: 3:00:03
2025-08-01 04:15:37.332 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:15:44.314 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:15:45.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:15:47.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3932
2025-08-01 04:15:47.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3888
2025-08-01 04:15:47.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2164
2025-08-01 04:15:47.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3328
2025-08-01 04:15:47.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.333
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:15:47.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:15:47.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:15:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:15:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:15:51.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:15:52.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:15:54.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:15:55.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:15:57.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:15:58.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:15:59.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:15:59.795 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:15:59.795 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 04:15:59.795 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:15:59.807 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.86 ms, Average inference time: 8.42 ms

2025-08-01 04:15:59.809 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:16:00.009 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:16:00.100 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch143
2025-08-01 04:16:03.469 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.762e-03, size: 320, ETA: 2:59:56
2025-08-01 04:16:06.984 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.172s, 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: 1.762e-03, size: 320, ETA: 2:59:52
2025-08-01 04:16:10.749 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.185s, 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: 1.761e-03, size: 544, ETA: 2:59:48
2025-08-01 04:16:14.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.2, lr: 1.761e-03, size: 544, ETA: 2:59:45
2025-08-01 04:16:18.313 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.760e-03, size: 320, ETA: 2:59:41
2025-08-01 04:16:22.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.186s, 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: 1.760e-03, size: 512, ETA: 2:59:38
2025-08-01 04:16:23.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:16:30.653 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:16:32.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:16:32.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3624
2025-08-01 04:16:33.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3004
2025-08-01 04:16:33.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1140
2025-08-01 04:16:33.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2589
2025-08-01 04:16:33.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:16:33.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:16:33.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-08-01 04:16:33.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-08-01 04:16:33.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.114
2025-08-01 04:16:33.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.259
2025-08-01 04:16:33.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:16:33.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:16:33.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:16:33.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:16:33.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:16:33.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:16:33.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:16:33.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:16:33.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:16:34.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:16:35.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:16:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:16:37.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:16:38.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:16:39.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:16:40.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:16:41.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:16:42.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:16:42.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 04:16:42.129 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 04:16:42.129 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:16:42.136 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.86 ms, Average inference time: 8.29 ms

2025-08-01 04:16:42.137 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:16:42.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:16:42.346 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch144
2025-08-01 04:16:46.084 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.184s, 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: 1.759e-03, size: 352, ETA: 2:59:33
2025-08-01 04:16:49.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.758e-03, size: 512, ETA: 2:59:29
2025-08-01 04:16:53.444 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.184s, 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: 1.758e-03, size: 512, ETA: 2:59:25
2025-08-01 04:16:57.120 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, 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: 1.757e-03, size: 320, ETA: 2:59:22
2025-08-01 04:17:01.058 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.193s, 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: 1.757e-03, size: 352, ETA: 2:59:19
2025-08-01 04:17:04.718 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.178s, 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: 1.756e-03, size: 416, ETA: 2:59:15
2025-08-01 04:17:06.336 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:17:13.121 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:17:17.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:17:21.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3664
2025-08-01 04:17:22.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3679
2025-08-01 04:17:22.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1780
2025-08-01 04:17:22.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3041
2025-08-01 04:17:22.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:17:22.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:17:22.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-08-01 04:17:22.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.178
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.304
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:17:22.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:17:22.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:17:26.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:17:31.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:17:35.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:17:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:17:44.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:17:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:17:52.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:17:57.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:18:01.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:18:01.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:18:01.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 04:18:01.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:18:01.607 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.88 ms, Average inference time: 8.27 ms

2025-08-01 04:18:01.609 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:18:01.686 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:18:01.772 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch145
2025-08-01 04:18:05.617 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.756e-03, size: 480, ETA: 2:59:10
2025-08-01 04:18:09.426 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.755e-03, size: 448, ETA: 2:59:06
2025-08-01 04:18:13.218 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.186s, 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: 1.754e-03, size: 320, ETA: 2:59:03
2025-08-01 04:18:16.991 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.754e-03, size: 512, ETA: 2:58:59
2025-08-01 04:18:20.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.753e-03, size: 544, ETA: 2:58:56
2025-08-01 04:18:24.636 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.195s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.753e-03, size: 288, ETA: 2:58:53
2025-08-01 04:18:26.326 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:18:33.199 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:18:34.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:18:34.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5011
2025-08-01 04:18:34.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4075
2025-08-01 04:18:34.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2364
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3816
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:18:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:18:34.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:18:34.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:18:34.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:18:34.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:18:34.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:18:35.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:18:36.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:18:37.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:18:37.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:18:38.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:18:39.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:18:40.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:18:40.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:18:41.448 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:18:41.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:18:41.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 04:18:41.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:18:41.456 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.85 ms, Average inference time: 8.34 ms

2025-08-01 04:18:41.459 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:18:41.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:18:41.618 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch146
2025-08-01 04:18:45.357 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.752e-03, size: 448, ETA: 2:58:47
2025-08-01 04:18:48.992 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.179s, 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: 1.751e-03, size: 288, ETA: 2:58:43
2025-08-01 04:18:52.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.177s, 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: 1.751e-03, size: 384, ETA: 2:58:39
2025-08-01 04:18:56.228 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.178s, 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: 1.750e-03, size: 576, ETA: 2:58:36
2025-08-01 04:19:00.314 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.201s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.750e-03, size: 512, ETA: 2:58:33
2025-08-01 04:19:04.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.749e-03, size: 544, ETA: 2:58:29
2025-08-01 04:19:05.806 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:19:12.639 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:19:14.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:19:16.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4900
2025-08-01 04:19:16.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3967
2025-08-01 04:19:16.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2212
2025-08-01 04:19:16.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3693
2025-08-01 04:19:16.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:19:16.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:19:16.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-01 04:19:16.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-01 04:19:16.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-08-01 04:19:16.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:19:16.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:19:18.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:19:19.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:19:21.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:19:23.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:19:25.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:19:26.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:19:28.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:19:30.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:19:32.255 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:19:32.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:19:32.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 04:19:32.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:19:32.282 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.86 ms, Average inference time: 8.32 ms

2025-08-01 04:19:32.290 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:19:32.378 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:19:32.465 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch147
2025-08-01 04:19:36.345 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/600, iter: 20/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.749e-03, size: 320, ETA: 2:58:25
2025-08-01 04:19:40.459 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.200s, data_time: 0.004s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.748e-03, size: 384, ETA: 2:58:22
2025-08-01 04:19:43.968 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.747e-03, size: 320, ETA: 2:58:18
2025-08-01 04:19:47.711 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.747e-03, size: 352, ETA: 2:58:14
2025-08-01 04:19:51.480 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.746e-03, size: 352, ETA: 2:58:10
2025-08-01 04:19:55.387 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.193s, 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: 1.746e-03, size: 480, ETA: 2:58:07
2025-08-01 04:19:57.136 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:20:03.966 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:20:05.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:20:06.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3313
2025-08-01 04:20:06.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3165
2025-08-01 04:20:06.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1554
2025-08-01 04:20:06.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2677
2025-08-01 04:20:06.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:20:06.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:20:06.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-08-01 04:20:06.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-01 04:20:06.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.155
2025-08-01 04:20:06.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.268
2025-08-01 04:20:06.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:20:06.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:20:06.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:20:06.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:20:06.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:20:06.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:20:06.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:20:06.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:20:06.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:20:07.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:20:08.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:20:09.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:20:10.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:20:11.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:20:13.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:20:14.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:20:15.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:20:16.335 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:20:16.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 04:20:16.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 04:20:16.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:20:16.350 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.86 ms, Average inference time: 8.36 ms

2025-08-01 04:20:16.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:20:16.464 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:20:16.599 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch148
2025-08-01 04:20:20.334 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.181s, 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: 1.745e-03, size: 256, ETA: 2:58:02
2025-08-01 04:20:24.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.744e-03, size: 576, ETA: 2:57:59
2025-08-01 04:20:28.018 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.193s, 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: 1.744e-03, size: 256, ETA: 2:57:55
2025-08-01 04:20:31.673 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.743e-03, size: 512, ETA: 2:57:51
2025-08-01 04:20:35.422 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.182s, 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: 1.743e-03, size: 384, ETA: 2:57:48
2025-08-01 04:20:39.175 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, 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: 1.742e-03, size: 480, ETA: 2:57:44
2025-08-01 04:20:40.852 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:20:47.605 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:20:49.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:20:50.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4791
2025-08-01 04:20:50.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3740
2025-08-01 04:20:50.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2305
2025-08-01 04:20:50.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3612
2025-08-01 04:20:50.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.231
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:20:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:20:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:20:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:20:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:20:51.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:20:53.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:20:54.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:20:55.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:20:57.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:20:58.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:20:59.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:21:01.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:21:02.588 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:21:02.589 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:21:02.589 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 04:21:02.589 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:21:02.596 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.88 ms, Average inference time: 8.32 ms

2025-08-01 04:21:02.598 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:21:02.678 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:21:02.762 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch149
2025-08-01 04:21:06.455 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.180s, 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: 1.741e-03, size: 576, ETA: 2:57:39
2025-08-01 04:21:10.455 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.198s, 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: 1.741e-03, size: 352, ETA: 2:57:36
2025-08-01 04:21:14.198 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.182s, 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: 1.740e-03, size: 384, ETA: 2:57:32
2025-08-01 04:21:18.013 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.189s, 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: 1.740e-03, size: 576, ETA: 2:57:29
2025-08-01 04:21:22.045 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/600, iter: 100/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.200s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.739e-03, size: 256, ETA: 2:57:26
2025-08-01 04:21:25.895 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.739e-03, size: 544, ETA: 2:57:23
2025-08-01 04:21:27.686 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:21:34.547 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:21:36.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:21:37.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4926
2025-08-01 04:21:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4473
2025-08-01 04:21:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2364
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3921
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.392
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:21:37.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:21:37.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:21:37.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:21:37.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:21:37.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:21:37.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:21:37.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:21:38.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:21:40.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:21:41.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:21:42.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:21:43.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:21:45.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:21:46.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:21:47.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:21:48.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:21:48.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:21:48.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 04:21:48.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:21:48.814 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.87 ms, Average inference time: 8.35 ms

2025-08-01 04:21:48.815 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:21:48.893 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:21:48.978 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch150
2025-08-01 04:21:52.578 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.738e-03, size: 448, ETA: 2:57:18
2025-08-01 04:21:56.337 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.737e-03, size: 352, ETA: 2:57:14
2025-08-01 04:21:59.988 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.737e-03, size: 576, ETA: 2:57:10
2025-08-01 04:22:03.991 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.199s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.736e-03, size: 256, ETA: 2:57:08
2025-08-01 04:22:07.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.736e-03, size: 320, ETA: 2:57:03
2025-08-01 04:22:11.369 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.2, lr: 1.735e-03, size: 384, ETA: 2:57:00
2025-08-01 04:22:13.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:22:19.886 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:22:22.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:22:24.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4331
2025-08-01 04:22:25.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3943
2025-08-01 04:22:25.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2020
2025-08-01 04:22:25.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3432
2025-08-01 04:22:25.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:22:25.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.343
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:22:25.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:22:25.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:22:27.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:22:30.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:22:32.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:22:34.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:22:37.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:22:39.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:22:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:22:44.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:22:46.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:22:46.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:22:46.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 04:22:46.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:22:46.745 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.87 ms, Average inference time: 8.38 ms

2025-08-01 04:22:46.746 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:22:46.870 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:22:46.978 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch151
2025-08-01 04:22:50.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.734e-03, size: 288, ETA: 2:56:54
2025-08-01 04:22:54.152 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.734e-03, size: 352, ETA: 2:56:50
2025-08-01 04:22:57.683 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.733e-03, size: 416, ETA: 2:56:46
2025-08-01 04:23:01.247 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.733e-03, size: 288, ETA: 2:56:42
2025-08-01 04:23:05.080 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.189s, 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: 1.732e-03, size: 416, ETA: 2:56:39
2025-08-01 04:23:08.675 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.731e-03, size: 288, ETA: 2:56:35
2025-08-01 04:23:10.262 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:23:17.128 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:23:19.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:23:20.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2955
2025-08-01 04:23:21.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2559
2025-08-01 04:23:21.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1383
2025-08-01 04:23:21.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2299
2025-08-01 04:23:21.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:23:21.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:23:21.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-08-01 04:23:21.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.256
2025-08-01 04:23:21.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.138
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.230
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:23:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:23:21.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:23:22.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:23:24.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:23:26.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:23:27.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:23:29.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:23:31.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:23:33.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:23:34.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:23:36.457 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:23:36.457 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 04:23:36.457 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-08-01 04:23:36.457 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:23:36.482 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.86 ms, Average inference time: 8.37 ms

2025-08-01 04:23:36.484 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:23:36.564 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:23:36.649 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch152
2025-08-01 04:23:40.387 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.185s, 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: 1.731e-03, size: 544, ETA: 2:56:29
2025-08-01 04:23:44.222 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.188s, 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: 1.730e-03, size: 544, ETA: 2:56:26
2025-08-01 04:23:48.021 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.729e-03, size: 288, ETA: 2:56:22
2025-08-01 04:23:51.652 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.175s, 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: 1.729e-03, size: 384, ETA: 2:56:18
2025-08-01 04:23:55.374 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.181s, 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: 1.728e-03, size: 448, ETA: 2:56:15
2025-08-01 04:23:59.060 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.728e-03, size: 416, ETA: 2:56:11
2025-08-01 04:24:00.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:24:07.547 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:24:10.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:24:13.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4990
2025-08-01 04:24:13.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4467
2025-08-01 04:24:13.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2479
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3978
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.248
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:24:13.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:24:13.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:24:13.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:24:13.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:24:13.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:24:13.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:24:13.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:24:16.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:24:19.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:24:22.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:24:25.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:24:28.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:24:31.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:24:34.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:24:36.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:24:39.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:24:39.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 04:24:39.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 04:24:39.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:24:39.864 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.87 ms, Average inference time: 8.39 ms

2025-08-01 04:24:39.865 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:24:39.946 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:24:40.030 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch153
2025-08-01 04:24:43.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.727e-03, size: 384, ETA: 2:56:05
2025-08-01 04:24:47.343 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/600, iter: 40/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.191s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.726e-03, size: 384, ETA: 2:56:01
2025-08-01 04:24:51.159 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.726e-03, size: 480, ETA: 2:55:58
2025-08-01 04:24:54.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.188s, 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: 1.725e-03, size: 416, ETA: 2:55:55
2025-08-01 04:24:58.853 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.191s, 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: 1.725e-03, size: 480, ETA: 2:55:51
2025-08-01 04:25:02.608 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/600, iter: 120/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.724e-03, size: 576, ETA: 2:55:48
2025-08-01 04:25:04.433 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:25:11.270 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:25:13.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:25:13.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4685
2025-08-01 04:25:14.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3471
2025-08-01 04:25:14.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2156
2025-08-01 04:25:14.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3437
2025-08-01 04:25:14.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:25:14.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:25:14.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-01 04:25:14.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-01 04:25:14.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-08-01 04:25:14.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 04:25:14.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:25:14.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:25:14.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:25:14.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:25:14.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:25:14.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:25:14.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:25:14.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:25:14.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:25:15.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:25:16.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:25:17.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:25:18.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:25:19.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:25:20.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:25:21.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:25:22.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:25:23.045 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:25:23.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:25:23.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 04:25:23.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:25:23.054 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.87 ms, Average inference time: 8.35 ms

2025-08-01 04:25:23.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:25:23.128 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:25:23.212 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch154
2025-08-01 04:25:26.850 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.723e-03, size: 480, ETA: 2:55:43
2025-08-01 04:25:30.541 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.181s, 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: 1.723e-03, size: 416, ETA: 2:55:39
2025-08-01 04:25:34.386 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.189s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.722e-03, size: 416, ETA: 2:55:36
2025-08-01 04:25:38.228 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.186s, 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: 1.722e-03, size: 480, ETA: 2:55:32
2025-08-01 04:25:41.939 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.182s, 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: 1.721e-03, size: 288, ETA: 2:55:28
2025-08-01 04:25:45.452 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.720e-03, size: 320, ETA: 2:55:24
2025-08-01 04:25:47.172 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:25:54.161 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:25:55.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:25:56.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4495
2025-08-01 04:25:56.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3780
2025-08-01 04:25:56.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2080
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3452
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.208
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:25:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:25:56.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:25:56.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:25:56.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:25:56.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:25:56.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:25:56.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:25:57.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:25:58.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:25:59.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:26:00.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:26:01.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:26:02.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:26:03.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:26:04.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:26:04.939 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:26:04.939 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 04:26:04.939 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 04:26:04.939 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:26:04.947 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.86 ms, Average inference time: 8.35 ms

2025-08-01 04:26:04.948 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:26:05.023 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:26:05.108 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch155
2025-08-01 04:26:08.932 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.720e-03, size: 512, ETA: 2:55:19
2025-08-01 04:26:12.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.187s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.719e-03, size: 416, ETA: 2:55:16
2025-08-01 04:26:16.382 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/600, iter: 60/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.718e-03, size: 512, ETA: 2:55:12
2025-08-01 04:26:20.040 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/600, iter: 80/129, gpu mem: 2082Mb, mem: 76.9Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.718e-03, size: 320, ETA: 2:55:08
2025-08-01 04:26:23.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.717e-03, size: 544, ETA: 2:55:05
2025-08-01 04:26:27.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.185s, 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: 1.717e-03, size: 320, ETA: 2:55:01
2025-08-01 04:26:29.264 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:26:36.144 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:26:37.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:26:38.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4915
2025-08-01 04:26:38.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4468
2025-08-01 04:26:38.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2500
2025-08-01 04:26:38.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3961
2025-08-01 04:26:38.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:26:38.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:26:38.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.250
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:26:38.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:26:38.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:26:38.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:26:39.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:26:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:26:41.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:26:42.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:26:44.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:26:45.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:26:46.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:26:47.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:26:48.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:26:48.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 04:26:48.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 04:26:48.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:26:48.426 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.86 ms, Average inference time: 8.29 ms

2025-08-01 04:26:48.427 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:26:48.506 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:26:48.593 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch156
2025-08-01 04:26:52.236 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.180s, 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: 1.716e-03, size: 448, ETA: 2:54:56
2025-08-01 04:26:55.963 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.715e-03, size: 512, ETA: 2:54:52
2025-08-01 04:26:59.727 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.715e-03, size: 384, ETA: 2:54:49
2025-08-01 04:27:03.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.187s, 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: 1.714e-03, size: 384, ETA: 2:54:45
2025-08-01 04:27:07.136 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.181s, 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: 1.714e-03, size: 352, ETA: 2:54:41
2025-08-01 04:27:10.888 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, 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: 1.713e-03, size: 384, ETA: 2:54:38
2025-08-01 04:27:12.621 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:27:19.434 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:27:20.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:27:21.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4372
2025-08-01 04:27:22.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2976
2025-08-01 04:27:22.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2092
2025-08-01 04:27:22.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3147
2025-08-01 04:27:22.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:27:22.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:27:22.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 04:27:22.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.298
2025-08-01 04:27:22.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-08-01 04:27:22.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.315
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:27:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:27:23.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:27:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:27:25.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:27:27.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:27:28.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:27:29.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:27:30.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:27:31.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:27:33.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:27:33.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 04:27:33.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 04:27:33.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:27:33.230 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.88 ms, Average inference time: 8.40 ms

2025-08-01 04:27:33.238 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:27:33.311 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:27:33.398 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch157
2025-08-01 04:27:36.989 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.177s, 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: 1.712e-03, size: 256, ETA: 2:54:32
2025-08-01 04:27:40.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.198s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.712e-03, size: 320, ETA: 2:54:30
2025-08-01 04:27:44.734 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.711e-03, size: 352, ETA: 2:54:26
2025-08-01 04:27:48.825 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.200s, 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: 1.710e-03, size: 320, ETA: 2:54:23
2025-08-01 04:27:52.665 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.191s, data_time: 0.001s, total_loss: 5.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.710e-03, size: 384, ETA: 2:54:20
2025-08-01 04:27:56.382 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.181s, 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: 1.709e-03, size: 480, ETA: 2:54:16
2025-08-01 04:27:58.100 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:28:04.839 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:28:07.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:28:09.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4824
2025-08-01 04:28:09.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4514
2025-08-01 04:28:09.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2602
2025-08-01 04:28:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3980
2025-08-01 04:28:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:28:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:28:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-01 04:28:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 04:28:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.260
2025-08-01 04:28:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-08-01 04:28:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:28:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:28:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:28:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:28:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:28:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:28:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:28:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:28:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:28:11.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:28:14.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:28:16.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:28:18.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:28:20.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:28:22.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:28:24.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:28:27.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:28:29.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:28:29.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:28:29.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 04:28:29.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:28:29.235 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.87 ms, Average inference time: 8.37 ms

2025-08-01 04:28:29.236 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:28:29.314 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:28:29.397 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch158
2025-08-01 04:28:33.009 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.176s, 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: 1.708e-03, size: 544, ETA: 2:54:11
2025-08-01 04:28:36.858 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.187s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.708e-03, size: 288, ETA: 2:54:07
2025-08-01 04:28:40.594 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.707e-03, size: 256, ETA: 2:54:04
2025-08-01 04:28:44.145 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.174s, 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: 1.707e-03, size: 256, ETA: 2:53:59
2025-08-01 04:28:47.784 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.706e-03, size: 576, ETA: 2:53:55
2025-08-01 04:28:51.671 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 1.0, lr: 1.705e-03, size: 384, ETA: 2:53:52
2025-08-01 04:28:53.281 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:29:00.277 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:29:01.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:29:02.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4020
2025-08-01 04:29:02.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3693
2025-08-01 04:29:02.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1929
2025-08-01 04:29:02.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3214
2025-08-01 04:29:02.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.321
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:29:02.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:29:02.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:29:02.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:29:02.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:29:03.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:29:05.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:29:06.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:29:07.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:29:08.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:29:09.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:29:10.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:29:11.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:29:12.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:29:12.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 04:29:12.895 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 04:29:12.895 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:29:12.909 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.85 ms, Average inference time: 8.26 ms

2025-08-01 04:29:12.910 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:29:13.026 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:29:13.143 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch159
2025-08-01 04:29:16.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.705e-03, size: 384, ETA: 2:53:46
2025-08-01 04:29:20.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.704e-03, size: 320, ETA: 2:53:43
2025-08-01 04:29:24.216 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.188s, 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: 1.703e-03, size: 480, ETA: 2:53:39
2025-08-01 04:29:28.072 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.703e-03, size: 512, ETA: 2:53:36
2025-08-01 04:29:31.885 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.1, lr: 1.702e-03, size: 544, ETA: 2:53:32
2025-08-01 04:29:35.655 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.702e-03, size: 352, ETA: 2:53:29
2025-08-01 04:29:37.331 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:29:44.170 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:29:45.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:29:46.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4648
2025-08-01 04:29:46.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3972
2025-08-01 04:29:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2404
2025-08-01 04:29:46.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3674
2025-08-01 04:29:46.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:29:46.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:29:46.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-01 04:29:46.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-01 04:29:46.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-08-01 04:29:46.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.367
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:29:46.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:29:47.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:29:49.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:29:50.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:29:51.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:29:52.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:29:53.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:29:54.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:29:55.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:29:56.910 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:29:56.910 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:29:56.910 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 04:29:56.910 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:29:56.918 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.62 ms, Average NMS time: 0.87 ms, Average inference time: 8.49 ms

2025-08-01 04:29:56.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:29:56.996 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:29:57.077 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch160
2025-08-01 04:30:00.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.187s, 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: 1.701e-03, size: 512, ETA: 2:53:24
2025-08-01 04:30:04.630 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.187s, data_time: 0.003s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.700e-03, size: 384, ETA: 2:53:20
2025-08-01 04:30:08.380 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.700e-03, size: 416, ETA: 2:53:16
2025-08-01 04:30:12.128 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.699e-03, size: 448, ETA: 2:53:13
2025-08-01 04:30:15.949 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.190s, 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: 1.698e-03, size: 288, ETA: 2:53:09
2025-08-01 04:30:19.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.181s, 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: 1.698e-03, size: 416, ETA: 2:53:06
2025-08-01 04:30:21.404 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:30:28.180 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:30:29.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:30:30.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4949
2025-08-01 04:30:31.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3916
2025-08-01 04:30:31.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2668
2025-08-01 04:30:31.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3845
2025-08-01 04:30:31.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:30:31.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:30:31.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.384
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:30:31.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:30:31.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:30:31.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:30:31.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:30:32.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:30:33.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:30:35.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:30:36.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:30:37.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:30:38.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:30:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:30:41.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:30:42.742 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:30:42.742 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:30:42.742 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 04:30:42.742 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:30:42.750 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.86 ms, Average inference time: 8.40 ms

2025-08-01 04:30:42.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:30:42.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:30:42.908 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch161
2025-08-01 04:30:46.742 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.188s, 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: 1.697e-03, size: 288, ETA: 2:53:01
2025-08-01 04:30:50.487 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.696e-03, size: 416, ETA: 2:52:58
2025-08-01 04:30:54.380 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.193s, 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: 1.696e-03, size: 544, ETA: 2:52:54
2025-08-01 04:30:58.067 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 1.0, lr: 1.695e-03, size: 448, ETA: 2:52:51
2025-08-01 04:31:01.784 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.695e-03, size: 544, ETA: 2:52:47
2025-08-01 04:31:05.342 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.694e-03, size: 320, ETA: 2:52:43
2025-08-01 04:31:06.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:31:13.937 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:31:15.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:31:17.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1556
2025-08-01 04:31:17.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2491
2025-08-01 04:31:17.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1083
2025-08-01 04:31:17.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1710
2025-08-01 04:31:17.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:31:17.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:31:17.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.156
2025-08-01 04:31:17.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 04:31:17.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.108
2025-08-01 04:31:17.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.171
2025-08-01 04:31:17.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:31:17.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:31:17.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:31:17.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:31:17.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:31:17.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:31:17.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:31:17.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:31:17.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:31:19.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:31:20.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:31:22.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:31:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:31:25.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:31:27.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:31:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:31:30.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:31:31.825 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:31:31.825 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 04:31:31.825 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.17
2025-08-01 04:31:31.825 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:31:31.850 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.85 ms, Average inference time: 8.39 ms

2025-08-01 04:31:31.852 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:31:31.941 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:31:32.027 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch162
2025-08-01 04:31:35.783 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.693e-03, size: 544, ETA: 2:52:38
2025-08-01 04:31:39.615 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.693e-03, size: 512, ETA: 2:52:34
2025-08-01 04:31:43.438 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.189s, 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: 1.692e-03, size: 416, ETA: 2:52:31
2025-08-01 04:31:47.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.188s, 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: 1.691e-03, size: 576, ETA: 2:52:28
2025-08-01 04:31:51.109 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.188s, 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: 1.691e-03, size: 544, ETA: 2:52:24
2025-08-01 04:31:54.938 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.189s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.690e-03, size: 384, ETA: 2:52:21
2025-08-01 04:31:56.582 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:32:03.422 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:32:05.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:32:06.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3599
2025-08-01 04:32:06.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3262
2025-08-01 04:32:06.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1391
2025-08-01 04:32:06.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2751
2025-08-01 04:32:06.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:32:06.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.326
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.139
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.275
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:32:06.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:32:06.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:32:06.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:32:06.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:32:06.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:32:07.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:32:09.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:32:10.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:32:11.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:32:12.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:32:14.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:32:15.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:32:16.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:32:17.911 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:32:17.911 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 04:32:17.911 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 04:32:17.911 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:32:17.918 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.87 ms, Average inference time: 8.36 ms

2025-08-01 04:32:17.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:32:18.009 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:32:18.112 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch163
2025-08-01 04:32:21.566 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.168s, 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: 1.689e-03, size: 256, ETA: 2:52:15
2025-08-01 04:32:25.256 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.689e-03, size: 416, ETA: 2:52:11
2025-08-01 04:32:28.915 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.688e-03, size: 256, ETA: 2:52:07
2025-08-01 04:32:32.360 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.688e-03, size: 256, ETA: 2:52:03
2025-08-01 04:32:35.921 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.175s, 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: 1.687e-03, size: 448, ETA: 2:51:59
2025-08-01 04:32:39.811 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 1.5, cls_loss: 0.7, lr: 1.686e-03, size: 352, ETA: 2:51:56
2025-08-01 04:32:41.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:32:48.363 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:32:50.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:32:51.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4186
2025-08-01 04:32:52.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3544
2025-08-01 04:32:52.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2024
2025-08-01 04:32:52.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3251
2025-08-01 04:32:52.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:32:52.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:32:52.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-01 04:32:52.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.325
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:32:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:32:53.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:32:55.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:32:56.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:32:58.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:32:59.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:33:01.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:33:03.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:33:04.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:33:06.082 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:33:06.082 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:33:06.082 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 04:33:06.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:33:06.106 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.86 ms, Average inference time: 8.33 ms

2025-08-01 04:33:06.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:33:06.193 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:33:06.289 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch164
2025-08-01 04:33:09.845 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.173s, 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: 1.685e-03, size: 512, ETA: 2:51:50
2025-08-01 04:33:13.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.186s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.685e-03, size: 480, ETA: 2:51:47
2025-08-01 04:33:17.323 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.2, lr: 1.684e-03, size: 288, ETA: 2:51:43
2025-08-01 04:33:20.892 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.3, lr: 1.684e-03, size: 320, ETA: 2:51:39
2025-08-01 04:33:24.660 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.187s, 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: 1.683e-03, size: 288, ETA: 2:51:35
2025-08-01 04:33:28.613 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.192s, 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: 1.682e-03, size: 448, ETA: 2:51:32
2025-08-01 04:33:30.289 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:33:37.262 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:33:41.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:33:43.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3327
2025-08-01 04:33:44.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3382
2025-08-01 04:33:44.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1702
2025-08-01 04:33:44.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2804
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.170
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.280
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:33:44.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:33:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:33:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:33:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:33:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:33:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:33:44.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:33:47.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:33:50.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:33:53.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:33:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:34:00.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:34:03.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:34:06.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:34:09.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:34:12.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:34:12.950 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 04:34:12.950 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 04:34:12.950 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:34:12.975 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.88 ms, Average inference time: 8.33 ms

2025-08-01 04:34:12.977 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:34:13.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:34:13.147 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch165
2025-08-01 04:34:16.845 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.682e-03, size: 256, ETA: 2:51:27
2025-08-01 04:34:20.584 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.681e-03, size: 352, ETA: 2:51:23
2025-08-01 04:34:24.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.680e-03, size: 576, ETA: 2:51:20
2025-08-01 04:34:28.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.680e-03, size: 480, ETA: 2:51:16
2025-08-01 04:34:31.841 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.188s, 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: 1.679e-03, size: 448, ETA: 2:51:13
2025-08-01 04:34:35.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.679e-03, size: 288, ETA: 2:51:09
2025-08-01 04:34:37.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:34:43.921 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:34:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:34:49.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4812
2025-08-01 04:34:49.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4016
2025-08-01 04:34:50.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2254
2025-08-01 04:34:50.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3694
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.225
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:34:50.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:34:50.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:34:50.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:34:50.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:34:52.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:34:55.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:34:58.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:35:01.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:35:03.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:35:06.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:35:09.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:35:12.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:35:14.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:35:14.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:35:14.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 04:35:14.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:35:14.934 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.88 ms, Average inference time: 8.39 ms

2025-08-01 04:35:14.935 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:35:15.010 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:35:15.093 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch166
2025-08-01 04:35:18.532 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.678e-03, size: 352, ETA: 2:51:02
2025-08-01 04:35:22.459 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.193s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.677e-03, size: 512, ETA: 2:50:59
2025-08-01 04:35:26.180 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, 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: 1.676e-03, size: 288, ETA: 2:50:56
2025-08-01 04:35:29.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.676e-03, size: 384, ETA: 2:50:52
2025-08-01 04:35:33.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.675e-03, size: 416, ETA: 2:50:48
2025-08-01 04:35:37.251 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.182s, 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: 1.675e-03, size: 448, ETA: 2:50:45
2025-08-01 04:35:38.965 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:35:45.762 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:35:47.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:35:49.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4675
2025-08-01 04:35:49.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4438
2025-08-01 04:35:49.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2410
2025-08-01 04:35:49.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3841
2025-08-01 04:35:49.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:35:49.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:35:49.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.241
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.384
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:35:49.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:35:51.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:35:52.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:35:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:35:55.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:35:57.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:35:59.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:36:00.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:36:02.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:36:03.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:36:03.960 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 04:36:03.960 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 04:36:03.960 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:36:03.987 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.86 ms, Average inference time: 8.20 ms

2025-08-01 04:36:03.989 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:36:04.066 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:36:04.149 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch167
2025-08-01 04:36:07.709 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.674e-03, size: 352, ETA: 2:50:39
2025-08-01 04:36:11.587 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.673e-03, size: 320, ETA: 2:50:36
2025-08-01 04:36:15.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.673e-03, size: 576, ETA: 2:50:32
2025-08-01 04:36:19.279 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.195s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.672e-03, size: 512, ETA: 2:50:29
2025-08-01 04:36:22.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.671e-03, size: 320, ETA: 2:50:25
2025-08-01 04:36:26.585 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.182s, 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: 1.671e-03, size: 256, ETA: 2:50:21
2025-08-01 04:36:28.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:36:34.993 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:36:38.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:36:40.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3977
2025-08-01 04:36:41.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3827
2025-08-01 04:36:41.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1904
2025-08-01 04:36:41.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3236
2025-08-01 04:36:41.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:36:41.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:36:41.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-08-01 04:36:41.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-01 04:36:41.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.190
2025-08-01 04:36:41.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.324
2025-08-01 04:36:41.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:36:41.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:36:41.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:36:41.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:36:41.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:36:41.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:36:41.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:36:41.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:36:41.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:36:43.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:36:46.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:36:49.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:36:52.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:36:54.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:36:57.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:37:00.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:37:02.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:37:05.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:37:05.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:37:05.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 04:37:05.628 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:37:05.654 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.86 ms, Average inference time: 8.37 ms

2025-08-01 04:37:05.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:37:05.784 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:37:05.871 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch168
2025-08-01 04:37:09.445 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.176s, 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: 1.670e-03, size: 416, ETA: 2:50:15
2025-08-01 04:37:13.154 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.669e-03, size: 352, ETA: 2:50:12
2025-08-01 04:37:17.126 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.196s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.669e-03, size: 384, ETA: 2:50:09
2025-08-01 04:37:20.994 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.668e-03, size: 448, ETA: 2:50:05
2025-08-01 04:37:24.731 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.667e-03, size: 352, ETA: 2:50:02
2025-08-01 04:37:28.312 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.667e-03, size: 256, ETA: 2:49:58
2025-08-01 04:37:29.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:37:36.770 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:37:37.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:37:38.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4382
2025-08-01 04:37:38.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3161
2025-08-01 04:37:38.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1923
2025-08-01 04:37:38.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3155
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.192
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.316
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:37:38.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:37:38.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:37:38.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:37:38.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:37:38.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:37:39.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:37:40.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:37:40.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:37:41.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:37:42.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:37:43.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:37:43.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:37:44.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:37:45.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:37:45.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 04:37:45.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 04:37:45.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:37:45.338 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.85 ms, Average inference time: 8.26 ms

2025-08-01 04:37:45.340 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:37:45.413 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:37:45.495 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch169
2025-08-01 04:37:49.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.176s, 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: 1.666e-03, size: 480, ETA: 2:49:52
2025-08-01 04:37:52.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.184s, 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: 1.665e-03, size: 480, ETA: 2:49:48
2025-08-01 04:37:56.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.186s, 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: 1.665e-03, size: 416, ETA: 2:49:45
2025-08-01 04:38:00.202 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.176s, 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: 1.664e-03, size: 448, ETA: 2:49:41
2025-08-01 04:38:04.171 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.197s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.663e-03, size: 544, ETA: 2:49:38
2025-08-01 04:38:07.902 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, 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: 1.663e-03, size: 480, ETA: 2:49:34
2025-08-01 04:38:09.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:38:16.528 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:38:18.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:38:20.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4380
2025-08-01 04:38:20.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4022
2025-08-01 04:38:20.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2133
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3511
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.351
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:38:20.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:38:20.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:38:20.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:38:20.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:38:20.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:38:20.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:38:20.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:38:20.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:38:22.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:38:24.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:38:25.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:38:27.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:38:29.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:38:31.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:38:33.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:38:34.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:38:36.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:38:36.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 04:38:36.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 04:38:36.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:38:36.604 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.87 ms, Average inference time: 8.38 ms

2025-08-01 04:38:36.605 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:38:36.684 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:38:36.781 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch170
2025-08-01 04:38:40.406 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.662e-03, size: 544, ETA: 2:49:29
2025-08-01 04:38:44.458 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.202s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.661e-03, size: 480, ETA: 2:49:26
2025-08-01 04:38:48.255 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.185s, 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: 1.661e-03, size: 448, ETA: 2:49:23
2025-08-01 04:38:52.045 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.185s, 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: 1.660e-03, size: 288, ETA: 2:49:19
2025-08-01 04:38:55.729 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, 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: 1.659e-03, size: 288, ETA: 2:49:16
2025-08-01 04:38:59.541 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.185s, 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: 1.659e-03, size: 416, ETA: 2:49:12
2025-08-01 04:39:01.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:39:07.995 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:39:10.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:39:12.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4239
2025-08-01 04:39:12.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3826
2025-08-01 04:39:12.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2088
2025-08-01 04:39:12.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3384
2025-08-01 04:39:12.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:39:12.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:39:12.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 04:39:12.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-01 04:39:12.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-08-01 04:39:12.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.338
2025-08-01 04:39:12.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:39:12.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:39:12.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:39:12.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:39:12.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:39:12.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:39:12.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:39:12.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:39:12.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:39:14.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:39:16.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:39:18.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:39:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:39:22.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:39:24.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:39:27.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:39:29.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:39:31.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:39:31.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:39:31.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 04:39:31.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:39:31.222 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.88 ms, Average inference time: 8.18 ms

2025-08-01 04:39:31.228 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:39:31.301 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:39:31.387 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch171
2025-08-01 04:39:34.989 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.179s, 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: 1.658e-03, size: 480, ETA: 2:49:07
2025-08-01 04:39:38.882 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.657e-03, size: 576, ETA: 2:49:03
2025-08-01 04:39:42.584 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.657e-03, size: 416, ETA: 2:49:00
2025-08-01 04:39:46.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.192s, 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: 1.656e-03, size: 576, ETA: 2:48:57
2025-08-01 04:39:50.309 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.186s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.655e-03, size: 416, ETA: 2:48:53
2025-08-01 04:39:53.928 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.179s, 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: 1.655e-03, size: 480, ETA: 2:48:49
2025-08-01 04:39:55.694 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:40:02.569 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:40:03.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:40:04.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3768
2025-08-01 04:40:04.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3023
2025-08-01 04:40:04.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1554
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2782
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.155
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.278
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:40:04.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:40:04.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:40:04.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:40:04.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:40:04.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:40:04.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:40:04.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:40:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:40:06.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:40:07.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:40:08.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:40:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:40:10.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:40:11.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:40:12.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:40:13.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:40:13.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 04:40:13.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 04:40:13.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:40:13.233 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.86 ms, Average inference time: 8.36 ms

2025-08-01 04:40:13.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:40:13.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:40:13.418 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch172
2025-08-01 04:40:17.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.654e-03, size: 256, ETA: 2:48:44
2025-08-01 04:40:20.966 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.196s, 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: 1.653e-03, size: 576, ETA: 2:48:41
2025-08-01 04:40:24.957 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.195s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.653e-03, size: 576, ETA: 2:48:38
2025-08-01 04:40:28.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.652e-03, size: 256, ETA: 2:48:34
2025-08-01 04:40:32.262 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.651e-03, size: 384, ETA: 2:48:30
2025-08-01 04:40:35.791 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.174s, 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: 1.651e-03, size: 416, ETA: 2:48:26
2025-08-01 04:40:37.449 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:40:44.301 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:40:46.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:40:48.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5089
2025-08-01 04:40:49.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4162
2025-08-01 04:40:49.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2914
2025-08-01 04:40:49.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4055
2025-08-01 04:40:49.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.291
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.406
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:40:49.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:40:49.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:40:49.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:40:49.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:40:49.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:40:51.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:40:53.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:40:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:40:58.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:41:00.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:41:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:41:04.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:41:06.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:41:09.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:41:09.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:41:09.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 04:41:09.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:41:09.313 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.86 ms, Average inference time: 8.32 ms

2025-08-01 04:41:09.314 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:41:09.429 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:41:09.580 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch173
2025-08-01 04:41:13.074 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.650e-03, size: 256, ETA: 2:48:20
2025-08-01 04:41:16.752 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.649e-03, size: 288, ETA: 2:48:16
2025-08-01 04:41:20.500 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.184s, 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: 1.649e-03, size: 288, ETA: 2:48:13
2025-08-01 04:41:24.359 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.189s, 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: 1.648e-03, size: 576, ETA: 2:48:09
2025-08-01 04:41:28.223 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 5.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.647e-03, size: 448, ETA: 2:48:06
2025-08-01 04:41:31.909 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.647e-03, size: 384, ETA: 2:48:02
2025-08-01 04:41:33.527 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:41:40.439 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:41:43.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:41:45.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4728
2025-08-01 04:41:46.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4523
2025-08-01 04:41:46.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2616
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3956
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.262
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:41:46.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:41:46.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:41:46.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:41:46.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:41:46.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:41:46.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:41:46.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:41:48.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:41:51.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:41:54.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:41:56.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:41:59.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:42:01.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:42:04.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:42:06.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:42:09.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:42:09.612 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 04:42:09.612 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 04:42:09.612 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:42:09.637 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.88 ms, Average inference time: 8.31 ms

2025-08-01 04:42:09.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:42:09.717 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:42:09.804 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch174
2025-08-01 04:42:13.420 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, 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: 1.646e-03, size: 544, ETA: 2:47:57
2025-08-01 04:42:17.253 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.645e-03, size: 320, ETA: 2:47:53
2025-08-01 04:42:21.004 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.645e-03, size: 448, ETA: 2:47:50
2025-08-01 04:42:24.782 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.187s, 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: 1.644e-03, size: 384, ETA: 2:47:46
2025-08-01 04:42:28.587 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.189s, 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: 1.643e-03, size: 576, ETA: 2:47:43
2025-08-01 04:42:32.373 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.186s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.643e-03, size: 448, ETA: 2:47:39
2025-08-01 04:42:34.102 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:42:40.892 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:42:44.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:42:46.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4271
2025-08-01 04:42:46.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3472
2025-08-01 04:42:47.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2090
2025-08-01 04:42:47.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3278
2025-08-01 04:42:47.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:42:47.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:42:47.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-08-01 04:42:47.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.328
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:42:47.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:42:47.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:42:49.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:42:52.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:42:55.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:42:57.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:43:00.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:43:02.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:43:05.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:43:08.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:43:10.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:43:10.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:43:10.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 04:43:10.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:43:10.814 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.88 ms, Average inference time: 8.31 ms

2025-08-01 04:43:10.816 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:43:10.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:43:10.981 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch175
2025-08-01 04:43:14.638 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.642e-03, size: 576, ETA: 2:47:34
2025-08-01 04:43:18.689 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.196s, data_time: 0.006s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.641e-03, size: 512, ETA: 2:47:31
2025-08-01 04:43:22.525 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.640e-03, size: 416, ETA: 2:47:28
2025-08-01 04:43:26.360 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.189s, 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: 1.640e-03, size: 576, ETA: 2:47:24
2025-08-01 04:43:30.259 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.192s, 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: 1.639e-03, size: 512, ETA: 2:47:21
2025-08-01 04:43:34.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.189s, 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: 1.639e-03, size: 384, ETA: 2:47:18
2025-08-01 04:43:35.728 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:43:42.516 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:43:44.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:43:45.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5001
2025-08-01 04:43:46.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4314
2025-08-01 04:43:46.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2399
2025-08-01 04:43:46.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3905
2025-08-01 04:43:46.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:43:46.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:43:46.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-01 04:43:46.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 04:43:46.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-08-01 04:43:46.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.390
2025-08-01 04:43:46.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:43:46.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:43:46.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:43:46.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:43:46.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:43:46.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:43:46.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:43:46.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:43:46.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:43:47.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:43:49.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:43:51.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:43:52.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:43:54.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:43:56.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:43:57.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:43:59.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:44:00.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:44:00.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 04:44:00.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 04:44:00.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:44:00.913 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.63 ms, Average NMS time: 0.89 ms, Average inference time: 8.52 ms

2025-08-01 04:44:00.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:44:01.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:44:01.162 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch176
2025-08-01 04:44:04.816 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.181s, data_time: 0.005s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.2, lr: 1.638e-03, size: 288, ETA: 2:47:12
2025-08-01 04:44:08.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.637e-03, size: 352, ETA: 2:47:09
2025-08-01 04:44:12.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.184s, 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: 1.636e-03, size: 512, ETA: 2:47:05
2025-08-01 04:44:16.085 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.636e-03, size: 384, ETA: 2:47:01
2025-08-01 04:44:19.790 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.3, lr: 1.635e-03, size: 544, ETA: 2:46:58
2025-08-01 04:44:23.602 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.634e-03, size: 320, ETA: 2:46:54
2025-08-01 04:44:25.246 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:44:31.939 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:44:33.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:44:33.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4530
2025-08-01 04:44:33.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3720
2025-08-01 04:44:33.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1883
2025-08-01 04:44:33.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3377
2025-08-01 04:44:33.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:44:33.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:44:33.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-01 04:44:33.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-01 04:44:33.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.188
2025-08-01 04:44:33.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.338
2025-08-01 04:44:33.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:44:33.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:44:33.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:44:33.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:44:33.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:44:33.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:44:33.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:44:33.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:44:33.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:44:34.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:44:35.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:44:36.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:44:37.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:44:38.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:44:39.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:44:40.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:44:40.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:44:41.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:44:41.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 04:44:41.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 04:44:41.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:44:41.714 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.84 ms, Average inference time: 8.25 ms

2025-08-01 04:44:41.715 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:44:41.790 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:44:41.893 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch177
2025-08-01 04:44:45.537 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.633e-03, size: 512, ETA: 2:46:48
2025-08-01 04:44:49.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.186s, 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: 1.633e-03, size: 384, ETA: 2:46:45
2025-08-01 04:44:52.936 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.177s, 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: 1.632e-03, size: 512, ETA: 2:46:41
2025-08-01 04:44:56.668 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.632e-03, size: 480, ETA: 2:46:37
2025-08-01 04:45:00.482 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.187s, 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: 1.631e-03, size: 448, ETA: 2:46:34
2025-08-01 04:45:04.163 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.180s, 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: 1.630e-03, size: 448, ETA: 2:46:30
2025-08-01 04:45:05.744 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:45:12.550 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:45:14.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:45:15.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3599
2025-08-01 04:45:15.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3023
2025-08-01 04:45:16.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1978
2025-08-01 04:45:16.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2866
2025-08-01 04:45:16.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:45:16.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.287
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:45:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:45:16.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:45:17.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:45:19.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:45:20.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:45:22.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:45:23.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:45:25.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:45:26.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:45:28.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:45:29.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:45:29.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 04:45:29.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 04:45:29.817 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:45:29.844 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.87 ms, Average inference time: 8.45 ms

2025-08-01 04:45:29.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:45:30.010 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:45:30.104 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch178
2025-08-01 04:45:33.711 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.629e-03, size: 352, ETA: 2:46:24
2025-08-01 04:45:37.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.629e-03, size: 448, ETA: 2:46:21
2025-08-01 04:45:41.326 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.194s, 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: 1.628e-03, size: 384, ETA: 2:46:17
2025-08-01 04:45:45.066 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.185s, 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: 1.627e-03, size: 480, ETA: 2:46:14
2025-08-01 04:45:48.800 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, 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: 1.627e-03, size: 416, ETA: 2:46:10
2025-08-01 04:45:52.591 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.186s, 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: 1.626e-03, size: 480, ETA: 2:46:07
2025-08-01 04:45:54.222 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:46:01.206 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:46:04.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:46:06.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4469
2025-08-01 04:46:06.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3498
2025-08-01 04:46:06.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1914
2025-08-01 04:46:06.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3294
2025-08-01 04:46:06.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:46:06.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:46:06.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 04:46:06.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-08-01 04:46:06.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 04:46:06.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.329
2025-08-01 04:46:06.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:46:06.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:46:06.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:46:06.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:46:06.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:46:06.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:46:06.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:46:06.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:46:06.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:46:09.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:46:11.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:46:13.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:46:15.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:46:17.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:46:20.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:46:22.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:46:24.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:46:26.571 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:46:26.571 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 04:46:26.571 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 04:46:26.571 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:46:26.600 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.59 ms, Average NMS time: 0.88 ms, Average inference time: 8.47 ms

2025-08-01 04:46:26.601 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:46:26.678 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:46:26.762 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch179
2025-08-01 04:46:30.460 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.625e-03, size: 256, ETA: 2:46:01
2025-08-01 04:46:34.116 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.625e-03, size: 352, ETA: 2:45:57
2025-08-01 04:46:37.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.624e-03, size: 384, ETA: 2:45:53
2025-08-01 04:46:41.289 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.177s, 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: 1.623e-03, size: 288, ETA: 2:45:49
2025-08-01 04:46:45.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, 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: 1.623e-03, size: 352, ETA: 2:45:45
2025-08-01 04:46:48.662 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.5, lr: 1.622e-03, size: 576, ETA: 2:45:42
2025-08-01 04:46:50.557 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:46:57.436 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:47:00.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:47:02.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4941
2025-08-01 04:47:03.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4538
2025-08-01 04:47:03.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2357
2025-08-01 04:47:03.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3945
2025-08-01 04:47:03.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:47:03.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:47:03.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-01 04:47:03.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:47:03.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:47:06.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:47:09.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:47:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:47:15.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:47:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:47:20.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:47:23.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:47:26.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:47:29.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:47:29.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:47:29.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 04:47:29.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:47:29.277 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.88 ms, Average inference time: 8.37 ms

2025-08-01 04:47:29.278 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:47:29.397 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:47:29.491 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch180
2025-08-01 04:47:33.117 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.177s, 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: 1.621e-03, size: 352, ETA: 2:45:36
2025-08-01 04:47:36.753 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.6, lr: 1.620e-03, size: 384, ETA: 2:45:33
2025-08-01 04:47:40.429 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.4, lr: 1.620e-03, size: 256, ETA: 2:45:29
2025-08-01 04:47:44.113 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.183s, 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: 1.619e-03, size: 576, ETA: 2:45:25
2025-08-01 04:47:48.133 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.199s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.618e-03, size: 320, ETA: 2:45:22
2025-08-01 04:47:51.818 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, 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: 1.618e-03, size: 512, ETA: 2:45:18
2025-08-01 04:47:53.497 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:48:00.243 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:48:01.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:48:02.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2903
2025-08-01 04:48:02.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2026
2025-08-01 04:48:02.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1385
2025-08-01 04:48:02.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2105
2025-08-01 04:48:02.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:48:02.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:48:02.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-01 04:48:02.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.203
2025-08-01 04:48:02.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.139
2025-08-01 04:48:02.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.210
2025-08-01 04:48:02.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:48:02.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:48:02.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:48:02.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:48:02.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:48:02.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:48:02.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:48:02.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:48:02.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:48:03.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:48:04.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:48:05.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:48:06.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:48:06.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:48:07.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:48:08.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:48:09.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:48:10.333 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:48:10.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 04:48:10.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.21
2025-08-01 04:48:10.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:48:10.342 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.85 ms, Average inference time: 8.27 ms

2025-08-01 04:48:10.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:48:10.471 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:48:10.561 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch181
2025-08-01 04:48:14.123 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.176s, data_time: 0.006s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.617e-03, size: 480, ETA: 2:45:13
2025-08-01 04:48:18.050 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.193s, 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: 1.616e-03, size: 576, ETA: 2:45:10
2025-08-01 04:48:21.951 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.193s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.616e-03, size: 576, ETA: 2:45:06
2025-08-01 04:48:25.652 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.615e-03, size: 352, ETA: 2:45:03
2025-08-01 04:48:29.368 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.183s, 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: 1.614e-03, size: 288, ETA: 2:44:59
2025-08-01 04:48:33.030 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.614e-03, size: 480, ETA: 2:44:55
2025-08-01 04:48:34.628 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:48:41.439 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:48:45.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:48:47.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4331
2025-08-01 04:48:48.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4069
2025-08-01 04:48:48.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2087
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3496
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.350
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:48:48.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:48:48.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:48:48.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:48:48.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:48:48.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:48:48.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:48:48.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:48:48.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:48:51.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:48:53.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:48:56.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:48:59.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:49:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:49:04.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:49:07.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:49:10.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:49:12.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:49:12.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 04:49:12.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 04:49:12.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:49:12.779 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.88 ms, Average inference time: 8.43 ms

2025-08-01 04:49:12.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:49:12.866 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:49:12.988 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch182
2025-08-01 04:49:16.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.195s, data_time: 0.003s, total_loss: 8.0, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.613e-03, size: 256, ETA: 2:44:50
2025-08-01 04:49:20.605 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, 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: 1.612e-03, size: 320, ETA: 2:44:46
2025-08-01 04:49:24.449 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.187s, 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: 1.611e-03, size: 544, ETA: 2:44:43
2025-08-01 04:49:28.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.194s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.611e-03, size: 384, ETA: 2:44:40
2025-08-01 04:49:32.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.183s, 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: 1.610e-03, size: 480, ETA: 2:44:36
2025-08-01 04:49:35.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.180s, 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: 1.609e-03, size: 384, ETA: 2:44:32
2025-08-01 04:49:37.328 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:49:44.113 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:49:45.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:49:45.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4841
2025-08-01 04:49:46.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3716
2025-08-01 04:49:46.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2259
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3605
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.226
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:49:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:49:46.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:49:46.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:49:46.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:49:46.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:49:46.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:49:47.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:49:47.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:49:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:49:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:49:50.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:49:51.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:49:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:49:53.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:49:53.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:49:53.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:49:53.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 04:49:53.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:49:53.925 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.63 ms, Average NMS time: 0.86 ms, Average inference time: 8.49 ms

2025-08-01 04:49:53.927 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:49:54.044 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:49:54.188 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch183
2025-08-01 04:49:57.808 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.608e-03, size: 352, ETA: 2:44:27
2025-08-01 04:50:01.397 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.608e-03, size: 416, ETA: 2:44:23
2025-08-01 04:50:05.257 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.190s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.607e-03, size: 320, ETA: 2:44:19
2025-08-01 04:50:09.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.0Gb, iter_time: 0.187s, 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: 1.606e-03, size: 448, ETA: 2:44:16
2025-08-01 04:50:12.824 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 9.7, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 4.6, cls_loss: 1.4, lr: 1.606e-03, size: 320, ETA: 2:44:12
2025-08-01 04:50:16.751 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.605e-03, size: 352, ETA: 2:44:09
2025-08-01 04:50:18.369 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:50:25.415 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:50:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:50:31.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4475
2025-08-01 04:50:31.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4252
2025-08-01 04:50:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1692
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3473
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.347
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:50:31.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:50:31.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:50:31.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:50:31.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:50:31.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:50:31.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:50:31.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:50:34.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:50:37.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:50:40.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:50:43.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:50:46.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:50:48.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:50:51.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:50:54.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:50:57.443 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:50:57.443 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:50:57.443 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 04:50:57.443 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:50:57.472 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.89 ms, Average inference time: 8.40 ms

2025-08-01 04:50:57.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:50:57.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:50:57.646 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch184
2025-08-01 04:51:01.222 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.175s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.604e-03, size: 512, ETA: 2:44:03
2025-08-01 04:51:04.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.181s, 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: 1.604e-03, size: 448, ETA: 2:43:59
2025-08-01 04:51:08.590 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.603e-03, size: 448, ETA: 2:43:56
2025-08-01 04:51:12.354 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.602e-03, size: 320, ETA: 2:43:52
2025-08-01 04:51:15.921 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.174s, 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: 1.602e-03, size: 320, ETA: 2:43:48
2025-08-01 04:51:19.536 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.601e-03, size: 416, ETA: 2:43:44
2025-08-01 04:51:21.216 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:51:27.980 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:51:39.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:51:47.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4670
2025-08-01 04:51:48.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4075
2025-08-01 04:51:48.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2339
2025-08-01 04:51:48.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3694
2025-08-01 04:51:48.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.234
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:51:48.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:51:48.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:51:48.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:51:48.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:51:48.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:51:57.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:52:07.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:52:17.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:52:26.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:52:35.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:52:44.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:52:53.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:53:02.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:53:11.251 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:53:11.252 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 04:53:11.252 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 04:53:11.252 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:53:11.280 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.94 ms, Average inference time: 8.30 ms

2025-08-01 04:53:11.282 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:53:11.360 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:53:11.446 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch185
2025-08-01 04:53:15.235 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.186s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.600e-03, size: 320, ETA: 2:43:39
2025-08-01 04:53:18.919 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.599e-03, size: 416, ETA: 2:43:35
2025-08-01 04:53:22.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.184s, 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: 1.599e-03, size: 352, ETA: 2:43:31
2025-08-01 04:53:26.408 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.598e-03, size: 320, ETA: 2:43:28
2025-08-01 04:53:30.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.178s, 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: 1.597e-03, size: 352, ETA: 2:43:24
2025-08-01 04:53:33.743 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.179s, 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: 1.597e-03, size: 416, ETA: 2:43:20
2025-08-01 04:53:35.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:53:42.221 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:53:44.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:53:46.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4425
2025-08-01 04:53:46.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3499
2025-08-01 04:53:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2051
2025-08-01 04:53:46.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3325
2025-08-01 04:53:46.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.205
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.332
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:53:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:53:46.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:53:46.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:53:46.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:53:46.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:53:49.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:53:51.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:53:53.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:53:55.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:53:57.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:53:59.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:54:02.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:54:04.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:54:06.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:54:06.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:54:06.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 04:54:06.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:54:06.282 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.85 ms, Average inference time: 8.19 ms

2025-08-01 04:54:06.290 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:54:06.367 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:54:06.452 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch186
2025-08-01 04:54:10.210 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.596e-03, size: 384, ETA: 2:43:14
2025-08-01 04:54:13.868 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.595e-03, size: 256, ETA: 2:43:11
2025-08-01 04:54:17.462 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, 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: 1.594e-03, size: 416, ETA: 2:43:07
2025-08-01 04:54:21.121 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.594e-03, size: 544, ETA: 2:43:03
2025-08-01 04:54:24.871 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.593e-03, size: 512, ETA: 2:42:59
2025-08-01 04:54:28.814 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.192s, 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: 1.592e-03, size: 352, ETA: 2:42:56
2025-08-01 04:54:30.494 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:54:37.268 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:54:40.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:54:42.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4319
2025-08-01 04:54:42.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4085
2025-08-01 04:54:42.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2145
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3516
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.215
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:54:42.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:54:42.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:54:42.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:54:42.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:54:42.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:54:42.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:54:42.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:54:42.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:54:44.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:54:47.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:54:49.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:54:51.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:54:53.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:54:55.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:54:57.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:54:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:55:01.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:55:01.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 04:55:01.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 04:55:01.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:55:01.878 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.86 ms, Average inference time: 8.23 ms

2025-08-01 04:55:01.881 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:55:01.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:55:02.042 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch187
2025-08-01 04:55:05.571 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, 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: 1.591e-03, size: 352, ETA: 2:42:50
2025-08-01 04:55:09.107 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.591e-03, size: 256, ETA: 2:42:46
2025-08-01 04:55:12.728 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.590e-03, size: 512, ETA: 2:42:42
2025-08-01 04:55:16.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.186s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.589e-03, size: 480, ETA: 2:42:39
2025-08-01 04:55:20.426 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.189s, 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: 1.589e-03, size: 544, ETA: 2:42:35
2025-08-01 04:55:24.377 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.192s, 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: 1.588e-03, size: 288, ETA: 2:42:32
2025-08-01 04:55:25.949 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:55:32.742 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:55:34.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:55:34.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4577
2025-08-01 04:55:35.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3368
2025-08-01 04:55:35.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1978
2025-08-01 04:55:35.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3308
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.331
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:55:35.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:55:35.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:55:35.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:55:35.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:55:35.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:55:35.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:55:35.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:55:36.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:55:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:55:38.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:55:39.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:55:40.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:55:41.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:55:42.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:55:43.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:55:44.121 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:55:44.121 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 04:55:44.121 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 04:55:44.121 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:55:44.129 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.85 ms, Average inference time: 8.41 ms

2025-08-01 04:55:44.131 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:55:44.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:55:44.287 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch188
2025-08-01 04:55:47.992 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.587e-03, size: 512, ETA: 2:42:26
2025-08-01 04:55:51.745 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.184s, 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: 1.586e-03, size: 480, ETA: 2:42:23
2025-08-01 04:55:55.443 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.586e-03, size: 448, ETA: 2:42:19
2025-08-01 04:55:59.132 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.181s, 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: 1.585e-03, size: 384, ETA: 2:42:15
2025-08-01 04:56:02.998 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.190s, 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: 1.584e-03, size: 544, ETA: 2:42:12
2025-08-01 04:56:06.852 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.191s, 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: 448, ETA: 2:42:09
2025-08-01 04:56:08.542 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:56:15.397 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:56:16.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:56:17.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5024
2025-08-01 04:56:17.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4398
2025-08-01 04:56:17.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2947
2025-08-01 04:56:17.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4123
2025-08-01 04:56:17.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:56:17.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:56:17.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-01 04:56:17.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-08-01 04:56:17.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-01 04:56:17.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-08-01 04:56:17.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:56:17.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:56:17.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:56:17.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:56:17.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:56:17.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:56:17.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:56:17.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:56:17.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:56:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:56:19.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:56:20.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:56:21.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:56:22.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:56:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:56:25.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:56:26.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:56:27.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:56:27.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 04:56:27.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 04:56:27.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:56:27.135 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.86 ms, Average inference time: 8.30 ms

2025-08-01 04:56:27.136 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:56:27.214 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:56:27.297 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch189
2025-08-01 04:56:31.076 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.188s, data_time: 0.005s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.583e-03, size: 288, ETA: 2:42:04
2025-08-01 04:56:34.715 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, 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: 1.582e-03, size: 352, ETA: 2:42:00
2025-08-01 04:56:38.384 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.182s, 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: 1.581e-03, size: 256, ETA: 2:41:56
2025-08-01 04:56:42.079 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.183s, 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: 1.581e-03, size: 384, ETA: 2:41:52
2025-08-01 04:56:45.875 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.182s, 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: 1.580e-03, size: 480, ETA: 2:41:49
2025-08-01 04:56:49.735 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.189s, 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: 1.579e-03, size: 384, ETA: 2:41:45
2025-08-01 04:56:51.398 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:56:58.209 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:56:59.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:57:00.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4894
2025-08-01 04:57:00.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4281
2025-08-01 04:57:00.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2454
2025-08-01 04:57:00.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3876
2025-08-01 04:57:00.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.245
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.388
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:57:00.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:57:00.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:57:00.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:57:00.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:57:00.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:57:00.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:57:01.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:57:02.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:57:04.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:57:05.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:57:06.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:57:07.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:57:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:57:09.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:57:10.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:57:10.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 04:57:10.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 04:57:10.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:57:10.462 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.84 ms, Average inference time: 8.24 ms

2025-08-01 04:57:10.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:57:10.542 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:57:10.628 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch190
2025-08-01 04:57:14.212 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.006s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.578e-03, size: 448, ETA: 2:41:40
2025-08-01 04:57:17.984 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.578e-03, size: 288, ETA: 2:41:36
2025-08-01 04:57:21.755 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.577e-03, size: 288, ETA: 2:41:32
2025-08-01 04:57:25.440 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.576e-03, size: 384, ETA: 2:41:28
2025-08-01 04:57:29.183 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, 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: 1.576e-03, size: 320, ETA: 2:41:25
2025-08-01 04:57:32.814 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, 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: 1.575e-03, size: 512, ETA: 2:41:21
2025-08-01 04:57:34.475 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:57:41.243 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:57:44.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:57:46.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4362
2025-08-01 04:57:46.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4159
2025-08-01 04:57:46.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1780
2025-08-01 04:57:46.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3434
2025-08-01 04:57:46.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:57:46.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.178
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.343
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:57:46.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:57:46.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:57:49.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:57:52.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:57:54.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:57:57.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:57:59.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:58:02.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:58:05.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:58:07.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:58:10.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:58:10.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 04:58:10.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 04:58:10.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:58:10.262 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.87 ms, Average inference time: 8.37 ms

2025-08-01 04:58:10.267 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:58:10.347 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:58:10.431 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch191
2025-08-01 04:58:14.014 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.574e-03, size: 352, ETA: 2:41:15
2025-08-01 04:58:17.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.573e-03, size: 448, ETA: 2:41:12
2025-08-01 04:58:21.590 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.189s, 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: 1.573e-03, size: 352, ETA: 2:41:08
2025-08-01 04:58:25.175 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.572e-03, size: 256, ETA: 2:41:04
2025-08-01 04:58:28.867 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, 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: 1.571e-03, size: 448, ETA: 2:41:01
2025-08-01 04:58:32.511 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.571e-03, size: 416, ETA: 2:40:57
2025-08-01 04:58:34.213 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:58:41.178 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:58:44.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:58:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4602
2025-08-01 04:58:47.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4096
2025-08-01 04:58:47.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2292
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3663
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.229
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.366
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:58:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:58:47.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:58:47.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:58:47.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:58:47.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:58:47.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:58:47.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:58:47.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:58:50.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:58:53.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:58:56.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 04:58:59.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 04:59:02.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 04:59:05.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 04:59:08.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 04:59:11.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 04:59:14.686 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 04:59:14.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 04:59:14.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 04:59:14.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 04:59:14.717 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.88 ms, Average inference time: 8.26 ms

2025-08-01 04:59:14.718 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:59:14.806 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:59:14.903 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch192
2025-08-01 04:59:18.631 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.570e-03, size: 448, ETA: 2:40:51
2025-08-01 04:59:22.428 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.569e-03, size: 448, ETA: 2:40:48
2025-08-01 04:59:25.993 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.568e-03, size: 480, ETA: 2:40:44
2025-08-01 04:59:29.574 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.5, lr: 1.568e-03, size: 416, ETA: 2:40:40
2025-08-01 04:59:33.448 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.189s, 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: 1.567e-03, size: 384, ETA: 2:40:37
2025-08-01 04:59:37.335 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.190s, 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: 1.566e-03, size: 448, ETA: 2:40:33
2025-08-01 04:59:38.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 04:59:45.819 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 04:59:48.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 04:59:51.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3279
2025-08-01 04:59:51.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2804
2025-08-01 04:59:51.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1604
2025-08-01 04:59:51.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2562
2025-08-01 04:59:51.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 04:59:51.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 04:59:51.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.160
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.256
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 04:59:51.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 04:59:51.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 04:59:54.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 04:59:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 04:59:59.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:00:01.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:00:04.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:00:06.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:00:09.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:00:11.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:00:14.501 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:00:14.502 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-08-01 05:00:14.503 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 05:00:14.503 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:00:14.538 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.87 ms, Average inference time: 8.31 ms

2025-08-01 05:00:14.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:00:14.687 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:00:14.803 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch193
2025-08-01 05:00:18.364 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.565e-03, size: 320, ETA: 2:40:28
2025-08-01 05:00:22.003 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, 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: 1.565e-03, size: 320, ETA: 2:40:24
2025-08-01 05:00:25.893 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.192s, 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: 1.564e-03, size: 544, ETA: 2:40:20
2025-08-01 05:00:29.924 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.200s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.563e-03, size: 480, ETA: 2:40:18
2025-08-01 05:00:33.887 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.196s, 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: 1.563e-03, size: 512, ETA: 2:40:14
2025-08-01 05:00:37.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.562e-03, size: 352, ETA: 2:40:11
2025-08-01 05:00:39.249 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:00:46.105 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:00:47.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:00:48.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4876
2025-08-01 05:00:48.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4039
2025-08-01 05:00:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2481
2025-08-01 05:00:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3799
2025-08-01 05:00:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:00:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:00:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-01 05:00:49.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.248
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:00:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:00:50.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:00:51.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:00:52.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:00:54.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:00:55.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:00:56.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:00:58.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:00:59.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:01:00.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:01:00.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:01:00.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 05:01:00.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:01:00.610 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.89 ms, Average inference time: 8.42 ms

2025-08-01 05:01:00.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:01:00.702 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:01:00.857 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch194
2025-08-01 05:01:04.467 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, 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: 416, ETA: 2:40:05
2025-08-01 05:01:08.200 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.186s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.560e-03, size: 416, ETA: 2:40:02
2025-08-01 05:01:11.947 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.560e-03, size: 480, ETA: 2:39:58
2025-08-01 05:01:15.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.559e-03, size: 256, ETA: 2:39:54
2025-08-01 05:01:19.291 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.558e-03, size: 256, ETA: 2:39:51
2025-08-01 05:01:22.944 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.6, lr: 1.558e-03, size: 448, ETA: 2:39:47
2025-08-01 05:01:24.542 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:01:31.683 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:01:37.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:01:40.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4772
2025-08-01 05:01:41.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3754
2025-08-01 05:01:41.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2782
2025-08-01 05:01:41.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3769
2025-08-01 05:01:41.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:01:41.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:01:41.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-08-01 05:01:41.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-01 05:01:41.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-08-01 05:01:41.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.377
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:01:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:01:46.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:01:51.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:01:55.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:02:00.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:02:04.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:02:09.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:02:13.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:02:18.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:02:22.685 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:02:22.685 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:02:22.685 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 05:02:22.685 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:02:22.710 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.87 ms, Average inference time: 8.36 ms

2025-08-01 05:02:22.714 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:02:22.799 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:02:22.888 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch195
2025-08-01 05:02:26.431 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.557e-03, size: 288, ETA: 2:39:41
2025-08-01 05:02:30.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.183s, 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: 1.556e-03, size: 384, ETA: 2:39:37
2025-08-01 05:02:34.120 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.196s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 1.2, lr: 1.555e-03, size: 576, ETA: 2:39:34
2025-08-01 05:02:37.965 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.554e-03, size: 480, ETA: 2:39:30
2025-08-01 05:02:41.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.554e-03, size: 352, ETA: 2:39:27
2025-08-01 05:02:45.473 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.553e-03, size: 480, ETA: 2:39:23
2025-08-01 05:02:47.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:02:53.917 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:03:03.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:03:08.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4720
2025-08-01 05:03:10.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4410
2025-08-01 05:03:10.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1189
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3440
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.119
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:03:10.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:03:10.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:03:10.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:03:10.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:03:10.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:03:10.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:03:18.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:03:26.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:03:34.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:03:42.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:03:49.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:03:58.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:04:05.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:04:13.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:04:21.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:04:21.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:04:21.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:04:21.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:04:21.456 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.92 ms, Average inference time: 8.35 ms

2025-08-01 05:04:21.456 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:04:21.534 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:04:21.621 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch196
2025-08-01 05:04:25.587 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.194s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.552e-03, size: 288, ETA: 2:39:18
2025-08-01 05:04:29.289 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.551e-03, size: 320, ETA: 2:39:15
2025-08-01 05:04:32.955 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, 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: 1.551e-03, size: 512, ETA: 2:39:11
2025-08-01 05:04:36.718 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.550e-03, size: 288, ETA: 2:39:07
2025-08-01 05:04:40.434 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.183s, 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: 1.549e-03, size: 288, ETA: 2:39:04
2025-08-01 05:04:44.197 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.186s, 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: 1.549e-03, size: 544, ETA: 2:39:00
2025-08-01 05:04:45.940 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:04:52.664 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:04:54.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:04:55.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4216
2025-08-01 05:04:55.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3892
2025-08-01 05:04:56.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2226
2025-08-01 05:04:56.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3445
2025-08-01 05:04:56.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:04:56.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:04:56.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:04:56.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:04:56.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:04:57.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:04:58.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:05:00.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:05:01.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:05:03.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:05:04.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:05:06.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:05:07.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:05:09.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:05:09.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 05:05:09.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:05:09.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:05:09.442 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.85 ms, Average inference time: 8.32 ms

2025-08-01 05:05:09.443 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:05:09.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:05:09.745 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch197
2025-08-01 05:05:13.474 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.548e-03, size: 256, ETA: 2:38:55
2025-08-01 05:05:17.072 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 1.0, lr: 1.547e-03, size: 320, ETA: 2:38:51
2025-08-01 05:05:20.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, 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: 1.546e-03, size: 288, ETA: 2:38:47
2025-08-01 05:05:24.603 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.4, cls_loss: 0.7, lr: 1.546e-03, size: 352, ETA: 2:38:44
2025-08-01 05:05:28.401 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.186s, 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: 1.545e-03, size: 544, ETA: 2:38:40
2025-08-01 05:05:32.207 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.544e-03, size: 384, ETA: 2:38:37
2025-08-01 05:05:33.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:05:40.736 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:05:41.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:05:42.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4146
2025-08-01 05:05:42.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3322
2025-08-01 05:05:42.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2345
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3271
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.234
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.327
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:05:42.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:05:42.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:05:42.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:05:42.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:05:42.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:05:42.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:05:42.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:05:43.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:05:44.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:05:45.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:05:45.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:05:46.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:05:47.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:05:48.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:05:49.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:05:49.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:05:49.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 05:05:49.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 05:05:49.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:05:49.796 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.85 ms, Average inference time: 8.31 ms

2025-08-01 05:05:49.797 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:05:49.870 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:05:50.008 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch198
2025-08-01 05:05:53.835 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.543e-03, size: 288, ETA: 2:38:31
2025-08-01 05:05:57.596 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.543e-03, size: 320, ETA: 2:38:28
2025-08-01 05:06:01.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.542e-03, size: 480, ETA: 2:38:25
2025-08-01 05:06:05.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 0.9, lr: 1.541e-03, size: 448, ETA: 2:38:21
2025-08-01 05:06:08.947 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.186s, 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: 1.540e-03, size: 288, ETA: 2:38:17
2025-08-01 05:06:12.717 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.186s, 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: 1.540e-03, size: 448, ETA: 2:38:14
2025-08-01 05:06:14.409 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:06:21.215 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:06:22.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:06:23.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3883
2025-08-01 05:06:23.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3292
2025-08-01 05:06:23.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2110
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3095
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.329
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:06:23.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:06:23.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:06:23.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:06:23.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:06:23.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:06:23.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:06:24.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:06:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:06:26.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:06:27.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:06:28.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:06:29.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:06:30.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:06:31.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:06:32.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:06:32.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 05:06:32.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 05:06:32.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:06:32.553 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.62 ms, Average NMS time: 0.85 ms, Average inference time: 8.47 ms

2025-08-01 05:06:32.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:06:32.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:06:32.744 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch199
2025-08-01 05:06:36.363 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, 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: 1.539e-03, size: 480, ETA: 2:38:08
2025-08-01 05:06:40.168 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.187s, 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: 1.538e-03, size: 320, ETA: 2:38:05
2025-08-01 05:06:43.870 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.181s, 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: 1.537e-03, size: 576, ETA: 2:38:01
2025-08-01 05:06:47.711 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.188s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.537e-03, size: 416, ETA: 2:37:57
2025-08-01 05:06:51.494 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.187s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.536e-03, size: 320, ETA: 2:37:54
2025-08-01 05:06:55.233 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.535e-03, size: 544, ETA: 2:37:50
2025-08-01 05:06:57.026 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:07:03.813 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:07:05.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:07:05.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3998
2025-08-01 05:07:05.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3985
2025-08-01 05:07:05.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2419
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3468
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.347
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:07:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:07:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:07:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:07:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:07:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:07:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:07:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:07:06.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:07:07.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:07:08.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:07:09.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:07:10.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:07:11.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:07:12.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:07:12.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:07:13.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:07:13.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:07:13.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 05:07:13.881 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:07:13.891 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.85 ms, Average inference time: 8.33 ms

2025-08-01 05:07:13.892 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:07:14.075 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:07:14.173 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch200
2025-08-01 05:07:14.173 | INFO     | yolox_microbt.core.trainer:before_epoch:208 - --->No mosaic aug now!
2025-08-01 05:07:14.173 | INFO     | yolox_microbt.core.trainer:before_epoch:210 - --->Add additional L1 loss now!
2025-08-01 05:07:14.174 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:07:17.582 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.8, cls_loss: 1.0, lr: 1.534e-03, size: 576, ETA: 2:37:45
2025-08-01 05:07:21.236 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 1.534e-03, size: 576, ETA: 2:37:41
2025-08-01 05:07:24.790 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 3.6, cls_loss: 0.7, lr: 1.533e-03, size: 448, ETA: 2:37:37
2025-08-01 05:07:28.205 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.6, lr: 1.532e-03, size: 480, ETA: 2:37:32
2025-08-01 05:07:31.606 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.531e-03, size: 416, ETA: 2:37:28
2025-08-01 05:07:35.035 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 10.2, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 5.3, cls_loss: 0.9, lr: 1.531e-03, size: 320, ETA: 2:37:23
2025-08-01 05:07:36.514 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:07:43.380 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:07:45.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:07:46.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3396
2025-08-01 05:07:46.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.0942
2025-08-01 05:07:47.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1222
2025-08-01 05:07:47.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1854
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.094
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.122
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.185
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:07:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:07:47.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:07:47.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:07:47.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:07:47.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:07:47.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:07:49.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:07:50.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:07:52.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:07:54.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:07:56.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:07:57.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:07:59.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:08:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:08:03.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:08:03.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 05:08:03.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.19
2025-08-01 05:08:03.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:08:03.335 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.87 ms, Average inference time: 8.42 ms

2025-08-01 05:08:03.336 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:08:03.433 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:08:03.531 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch201
2025-08-01 05:08:06.750 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, 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: 1.530e-03, size: 480, ETA: 2:37:17
2025-08-01 05:08:10.012 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 1.529e-03, size: 384, ETA: 2:37:12
2025-08-01 05:08:13.357 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 0.7, lr: 1.528e-03, size: 480, ETA: 2:37:08
2025-08-01 05:08:16.841 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 1.528e-03, size: 448, ETA: 2:37:03
2025-08-01 05:08:20.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.156s, 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: 1.527e-03, size: 384, ETA: 2:36:59
2025-08-01 05:08:23.312 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.526e-03, size: 416, ETA: 2:36:54
2025-08-01 05:08:24.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:08:31.528 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:08:32.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:08:32.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3983
2025-08-01 05:08:33.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3428
2025-08-01 05:08:33.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2136
2025-08-01 05:08:33.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3182
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.214
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.318
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:08:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:08:33.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:08:33.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:08:33.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:08:33.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:08:33.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:08:33.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:08:34.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:08:35.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:08:35.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:08:36.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:08:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:08:37.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:08:38.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:08:39.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:08:39.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:08:39.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 05:08:39.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:08:39.124 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.83 ms, Average inference time: 8.25 ms

2025-08-01 05:08:39.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:08:39.202 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:08:39.283 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch202
2025-08-01 05:08:42.546 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.9, lr: 1.525e-03, size: 352, ETA: 2:36:48
2025-08-01 05:08:45.897 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 1.2, lr: 1.524e-03, size: 384, ETA: 2:36:43
2025-08-01 05:08:49.048 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.524e-03, size: 320, ETA: 2:36:39
2025-08-01 05:08:52.751 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.523e-03, size: 480, ETA: 2:36:35
2025-08-01 05:08:56.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.522e-03, size: 384, ETA: 2:36:31
2025-08-01 05:08:59.517 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 1.522e-03, size: 256, ETA: 2:36:26
2025-08-01 05:09:00.962 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:09:07.868 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:09:08.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:09:09.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4630
2025-08-01 05:09:09.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3531
2025-08-01 05:09:09.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2598
2025-08-01 05:09:09.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3586
2025-08-01 05:09:09.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:09:09.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:09:09.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-01 05:09:09.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-08-01 05:09:09.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.260
2025-08-01 05:09:09.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-08-01 05:09:09.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:09:09.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:09:09.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:09:09.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:09:09.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:09:09.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:09:09.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:09:09.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:09:09.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:09:09.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:09:10.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:09:11.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:09:11.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:09:12.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:09:12.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:09:13.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:09:14.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:09:14.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:09:14.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:09:14.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 05:09:14.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:09:14.739 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.86 ms, Average inference time: 8.46 ms

2025-08-01 05:09:14.741 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:09:14.826 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:09:14.986 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch203
2025-08-01 05:09:18.105 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, 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: 1.521e-03, size: 480, ETA: 2:36:19
2025-08-01 05:09:21.546 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 9.9, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 4.9, cls_loss: 0.9, lr: 1.520e-03, size: 544, ETA: 2:36:15
2025-08-01 05:09:24.925 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 1.3, cls_loss: 0.7, lr: 1.519e-03, size: 544, ETA: 2:36:11
2025-08-01 05:09:28.294 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.163s, 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: 1.519e-03, size: 288, ETA: 2:36:06
2025-08-01 05:09:31.597 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.518e-03, size: 288, ETA: 2:36:02
2025-08-01 05:09:35.116 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 1.517e-03, size: 576, ETA: 2:35:58
2025-08-01 05:09:36.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:09:43.320 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:09:44.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:09:44.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3907
2025-08-01 05:09:44.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3028
2025-08-01 05:09:45.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1733
2025-08-01 05:09:45.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2889
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.173
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.289
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:09:45.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:09:45.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:09:45.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:09:45.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:09:45.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:09:45.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:09:45.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:09:46.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:09:47.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:09:47.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:09:48.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:09:48.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:09:49.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:09:50.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:09:50.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:09:50.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 05:09:50.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 05:09:50.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:09:50.734 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.84 ms, Average inference time: 8.19 ms

2025-08-01 05:09:50.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:09:50.829 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:09:50.970 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch204
2025-08-01 05:09:54.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 1.516e-03, size: 512, ETA: 2:35:51
2025-08-01 05:09:57.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 8.2, iou_loss: 1.6, l1_loss: 0.9, conf_loss: 3.9, cls_loss: 1.9, lr: 1.515e-03, size: 448, ETA: 2:35:47
2025-08-01 05:10:00.966 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, 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: 1.515e-03, size: 352, ETA: 2:35:42
2025-08-01 05:10:04.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 3.3, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 1.514e-03, size: 256, ETA: 2:35:38
2025-08-01 05:10:07.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.9, lr: 1.513e-03, size: 320, ETA: 2:35:34
2025-08-01 05:10:10.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 1.513e-03, size: 416, ETA: 2:35:29
2025-08-01 05:10:12.485 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:10:19.216 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:10:19.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:10:20.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4026
2025-08-01 05:10:20.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3077
2025-08-01 05:10:20.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1618
2025-08-01 05:10:20.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2907
2025-08-01 05:10:20.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:10:20.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:10:20.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-01 05:10:20.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-08-01 05:10:20.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.162
2025-08-01 05:10:20.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.291
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:10:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:10:21.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:10:21.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:10:22.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:10:22.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:10:23.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:10:23.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:10:24.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:10:25.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:10:25.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:10:25.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:10:25.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 05:10:25.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:10:25.638 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.83 ms, Average inference time: 8.21 ms

2025-08-01 05:10:25.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:10:25.722 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:10:25.800 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch205
2025-08-01 05:10:29.093 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.7, lr: 1.512e-03, size: 512, ETA: 2:35:23
2025-08-01 05:10:32.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.6, cls_loss: 0.8, lr: 1.511e-03, size: 256, ETA: 2:35:18
2025-08-01 05:10:35.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.510e-03, size: 448, ETA: 2:35:14
2025-08-01 05:10:39.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.509e-03, size: 256, ETA: 2:35:09
2025-08-01 05:10:42.516 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.170s, 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: 1.509e-03, size: 512, ETA: 2:35:05
2025-08-01 05:10:45.846 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.508e-03, size: 576, ETA: 2:35:01
2025-08-01 05:10:47.494 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:10:54.278 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:10:54.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:10:55.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3827
2025-08-01 05:10:55.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2622
2025-08-01 05:10:55.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1729
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2726
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.262
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.173
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.273
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:10:55.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:10:55.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:10:55.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:10:55.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:10:55.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:10:55.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:10:55.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:10:55.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:10:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:10:56.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:10:57.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:10:57.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:10:58.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:10:58.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:10:59.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:10:59.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:11:00.268 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:11:00.268 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 05:11:00.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 05:11:00.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:11:00.276 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.82 ms, Average inference time: 8.27 ms

2025-08-01 05:11:00.277 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:11:00.354 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:11:00.433 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch206
2025-08-01 05:11:03.847 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.507e-03, size: 544, ETA: 2:34:55
2025-08-01 05:11:07.157 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.506e-03, size: 256, ETA: 2:34:50
2025-08-01 05:11:10.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 1.0, lr: 1.506e-03, size: 576, ETA: 2:34:46
2025-08-01 05:11:14.131 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 1.1, lr: 1.505e-03, size: 448, ETA: 2:34:42
2025-08-01 05:11:17.326 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.504e-03, size: 384, ETA: 2:34:37
2025-08-01 05:11:20.547 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.503e-03, size: 352, ETA: 2:34:33
2025-08-01 05:11:22.057 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:11:28.793 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:11:29.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:11:30.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4501
2025-08-01 05:11:30.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4129
2025-08-01 05:11:30.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2045
2025-08-01 05:11:30.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3558
2025-08-01 05:11:30.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:11:30.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:11:30.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.204
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:11:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:11:30.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:11:30.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:11:30.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:11:31.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:11:32.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:11:33.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:11:34.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:11:35.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:11:36.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:11:37.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:11:38.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:11:38.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:11:38.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:11:38.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 05:11:38.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:11:38.896 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.82 ms, Average inference time: 8.31 ms

2025-08-01 05:11:38.897 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:11:38.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:11:39.056 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch207
2025-08-01 05:11:42.306 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 0.8, lr: 1.502e-03, size: 416, ETA: 2:34:26
2025-08-01 05:11:45.671 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.3, l1_loss: 0.3, conf_loss: 1.8, cls_loss: 0.5, lr: 1.502e-03, size: 576, ETA: 2:34:22
2025-08-01 05:11:49.034 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.0, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 1.501e-03, size: 256, ETA: 2:34:18
2025-08-01 05:11:52.291 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 1.2, lr: 1.500e-03, size: 320, ETA: 2:34:13
2025-08-01 05:11:55.740 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.499e-03, size: 320, ETA: 2:34:09
2025-08-01 05:11:59.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.8, lr: 1.499e-03, size: 544, ETA: 2:34:04
2025-08-01 05:12:00.717 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:12:07.334 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:12:07.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:12:07.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3853
2025-08-01 05:12:08.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2739
2025-08-01 05:12:08.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1347
2025-08-01 05:12:08.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2647
2025-08-01 05:12:08.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:12:08.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:12:08.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-01 05:12:08.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.274
2025-08-01 05:12:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.135
2025-08-01 05:12:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.265
2025-08-01 05:12:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:12:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:12:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:12:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:12:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:12:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:12:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:12:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:12:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:12:08.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:12:08.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:12:08.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:12:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:12:09.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:12:09.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:12:09.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:12:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:12:10.512 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:12:10.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 05:12:10.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 05:12:10.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:12:10.519 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.73 ms, Average inference time: 8.13 ms

2025-08-01 05:12:10.520 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:12:10.595 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:12:10.703 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch208
2025-08-01 05:12:14.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 1.498e-03, size: 544, ETA: 2:33:58
2025-08-01 05:12:17.454 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 1.497e-03, size: 576, ETA: 2:33:54
2025-08-01 05:12:20.950 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 1.496e-03, size: 480, ETA: 2:33:50
2025-08-01 05:12:24.261 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.6, lr: 1.496e-03, size: 480, ETA: 2:33:46
2025-08-01 05:12:27.532 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.495e-03, size: 352, ETA: 2:33:41
2025-08-01 05:12:30.765 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.7, lr: 1.494e-03, size: 256, ETA: 2:33:37
2025-08-01 05:12:32.211 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:12:38.940 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:12:39.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:12:39.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3518
2025-08-01 05:12:39.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2748
2025-08-01 05:12:39.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1851
2025-08-01 05:12:39.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2706
2025-08-01 05:12:39.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:12:39.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:12:39.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-01 05:12:39.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-08-01 05:12:39.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.185
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.271
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:12:39.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:12:39.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:12:40.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:12:40.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:12:40.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:12:41.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:12:41.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:12:41.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:12:42.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:12:42.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:12:43.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:12:43.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 05:12:43.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 05:12:43.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:12:43.190 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.82 ms, Average inference time: 8.23 ms

2025-08-01 05:12:43.191 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:12:43.297 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:12:43.388 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch209
2025-08-01 05:12:46.542 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.004s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.7, lr: 1.493e-03, size: 512, ETA: 2:33:30
2025-08-01 05:12:50.048 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 1.492e-03, size: 448, ETA: 2:33:26
2025-08-01 05:12:53.419 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.7, lr: 1.492e-03, size: 512, ETA: 2:33:22
2025-08-01 05:12:56.747 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.491e-03, size: 512, ETA: 2:33:17
2025-08-01 05:13:00.174 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.490e-03, size: 448, ETA: 2:33:13
2025-08-01 05:13:03.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 20.7, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 20.7, cls_loss: 0.0, lr: 1.490e-03, size: 480, ETA: 2:33:09
2025-08-01 05:13:05.290 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:13:12.093 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:13:12.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:13:12.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2789
2025-08-01 05:13:12.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2539
2025-08-01 05:13:12.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1239
2025-08-01 05:13:12.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2189
2025-08-01 05:13:12.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:13:12.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:13:12.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.279
2025-08-01 05:13:12.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-01 05:13:12.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.124
2025-08-01 05:13:12.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.219
2025-08-01 05:13:12.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:13:12.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:13:12.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:13:12.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:13:12.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:13:12.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:13:12.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:13:12.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:13:12.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:13:13.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:13:13.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:13:13.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:13:14.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:13:14.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:13:14.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:13:14.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:13:15.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:13:15.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:13:15.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 05:13:15.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-08-01 05:13:15.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:13:15.611 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.62 ms, Average NMS time: 0.74 ms, Average inference time: 8.36 ms

2025-08-01 05:13:15.613 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:13:15.696 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:13:15.778 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch210
2025-08-01 05:13:19.185 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 10.1, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 5.2, cls_loss: 0.9, lr: 1.488e-03, size: 480, ETA: 2:33:03
2025-08-01 05:13:22.702 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.488e-03, size: 416, ETA: 2:32:59
2025-08-01 05:13:25.981 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.487e-03, size: 288, ETA: 2:32:55
2025-08-01 05:13:29.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.486e-03, size: 320, ETA: 2:32:50
2025-08-01 05:13:32.664 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 1.486e-03, size: 544, ETA: 2:32:46
2025-08-01 05:13:35.992 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.485e-03, size: 448, ETA: 2:32:41
2025-08-01 05:13:37.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:13:44.200 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:13:45.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:13:47.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3947
2025-08-01 05:13:47.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3070
2025-08-01 05:13:47.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1908
2025-08-01 05:13:47.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2975
2025-08-01 05:13:47.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:13:47.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:13:47.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-01 05:13:47.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-01 05:13:47.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 05:13:47.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.297
2025-08-01 05:13:47.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:13:47.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:13:47.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:13:47.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:13:47.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:13:47.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:13:47.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:13:47.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:13:47.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:13:48.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:13:50.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:13:51.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:13:53.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:13:54.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:13:56.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:13:57.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:13:59.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:14:00.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:14:00.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:14:00.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 05:14:00.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:14:00.489 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.86 ms, Average inference time: 8.21 ms

2025-08-01 05:14:00.491 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:14:00.618 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:14:00.697 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch211
2025-08-01 05:14:03.848 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 10.2, iou_loss: 3.4, l1_loss: 1.4, conf_loss: 4.5, cls_loss: 0.8, lr: 1.484e-03, size: 288, ETA: 2:32:35
2025-08-01 05:14:07.199 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 1.483e-03, size: 384, ETA: 2:32:31
2025-08-01 05:14:10.465 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.482e-03, size: 480, ETA: 2:32:26
2025-08-01 05:14:13.754 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 1.482e-03, size: 512, ETA: 2:32:22
2025-08-01 05:14:17.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.481e-03, size: 512, ETA: 2:32:18
2025-08-01 05:14:20.572 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 1.5, conf_loss: 2.9, cls_loss: 0.8, lr: 1.480e-03, size: 576, ETA: 2:32:14
2025-08-01 05:14:22.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:14:28.767 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:14:29.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:14:30.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4840
2025-08-01 05:14:30.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4129
2025-08-01 05:14:30.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2759
2025-08-01 05:14:30.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3909
2025-08-01 05:14:30.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:14:30.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:14:30.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-01 05:14:30.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 05:14:30.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.391
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:14:30.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:14:31.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:14:32.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:14:33.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:14:33.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:14:34.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:14:35.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:14:36.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:14:37.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:14:38.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:14:38.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 05:14:38.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 05:14:38.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:14:38.064 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.82 ms, Average inference time: 8.24 ms

2025-08-01 05:14:38.066 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:14:38.148 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:14:38.226 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch212
2025-08-01 05:14:41.564 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.479e-03, size: 288, ETA: 2:32:07
2025-08-01 05:14:44.891 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 1.478e-03, size: 480, ETA: 2:32:03
2025-08-01 05:14:48.173 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 1.478e-03, size: 352, ETA: 2:31:59
2025-08-01 05:14:51.345 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 1.477e-03, size: 320, ETA: 2:31:54
2025-08-01 05:14:54.589 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.476e-03, size: 288, ETA: 2:31:50
2025-08-01 05:14:57.786 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.8, lr: 1.476e-03, size: 416, ETA: 2:31:45
2025-08-01 05:14:59.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:15:06.244 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:15:07.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:15:08.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3869
2025-08-01 05:15:08.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3726
2025-08-01 05:15:08.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1141
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2912
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.114
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.291
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:15:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:15:08.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:15:08.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:15:08.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:15:08.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:15:08.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:15:08.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:15:09.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:15:10.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:15:11.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:15:12.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:15:13.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:15:14.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:15:15.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:15:16.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:15:18.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:15:18.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:15:18.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 05:15:18.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:15:18.065 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.84 ms, Average inference time: 8.19 ms

2025-08-01 05:15:18.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:15:18.164 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:15:18.261 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch213
2025-08-01 05:15:21.506 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.474e-03, size: 480, ETA: 2:31:39
2025-08-01 05:15:24.819 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 1.474e-03, size: 384, ETA: 2:31:34
2025-08-01 05:15:28.113 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.4, conf_loss: 2.2, cls_loss: 0.7, lr: 1.473e-03, size: 416, ETA: 2:31:30
2025-08-01 05:15:31.242 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.472e-03, size: 288, ETA: 2:31:25
2025-08-01 05:15:34.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.472e-03, size: 544, ETA: 2:31:21
2025-08-01 05:15:37.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 1.1, lr: 1.471e-03, size: 256, ETA: 2:31:16
2025-08-01 05:15:39.399 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:15:46.278 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:15:46.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:15:47.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3901
2025-08-01 05:15:47.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3674
2025-08-01 05:15:47.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1886
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3154
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.189
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.315
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:15:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:15:47.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:15:47.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:15:47.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:15:47.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:15:47.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:15:47.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:15:47.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:15:48.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:15:48.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:15:49.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:15:49.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:15:50.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:15:50.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:15:51.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:15:51.658 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:15:51.658 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 05:15:51.658 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 05:15:51.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:15:51.674 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.81 ms, Average inference time: 8.22 ms

2025-08-01 05:15:51.675 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:15:51.789 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:15:51.900 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch214
2025-08-01 05:15:55.071 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, 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: 1.470e-03, size: 288, ETA: 2:31:10
2025-08-01 05:15:58.341 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.469e-03, size: 256, ETA: 2:31:05
2025-08-01 05:16:01.729 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 1.468e-03, size: 352, ETA: 2:31:01
2025-08-01 05:16:04.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 3.5, cls_loss: 0.9, lr: 1.468e-03, size: 512, ETA: 2:30:57
2025-08-01 05:16:08.306 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.467e-03, size: 480, ETA: 2:30:53
2025-08-01 05:16:11.685 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 0.7, lr: 1.466e-03, size: 448, ETA: 2:30:48
2025-08-01 05:16:13.184 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:16:19.919 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:16:21.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:16:21.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2486
2025-08-01 05:16:21.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1114
2025-08-01 05:16:22.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0362
2025-08-01 05:16:22.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1321
2025-08-01 05:16:22.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:16:22.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:16:22.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 05:16:22.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.111
2025-08-01 05:16:22.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.036
2025-08-01 05:16:22.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.132
2025-08-01 05:16:22.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:16:22.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:16:22.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:16:22.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:16:22.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:16:22.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:16:22.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:16:22.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:16:22.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:16:23.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:16:24.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:16:25.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:16:26.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:16:27.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:16:28.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:16:29.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:16:30.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:16:31.187 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:16:31.188 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.06
2025-08-01 05:16:31.188 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.13
2025-08-01 05:16:31.189 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:16:31.200 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.82 ms, Average inference time: 8.21 ms

2025-08-01 05:16:31.201 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:16:31.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:16:31.440 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch215
2025-08-01 05:16:34.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 1.465e-03, size: 416, ETA: 2:30:42
2025-08-01 05:16:37.802 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.9, lr: 1.464e-03, size: 576, ETA: 2:30:37
2025-08-01 05:16:41.074 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 1.464e-03, size: 288, ETA: 2:30:33
2025-08-01 05:16:44.620 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.7, lr: 1.463e-03, size: 576, ETA: 2:30:29
2025-08-01 05:16:48.163 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 1.0, lr: 1.462e-03, size: 544, ETA: 2:30:25
2025-08-01 05:16:51.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.461e-03, size: 576, ETA: 2:30:21
2025-08-01 05:16:53.185 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:17:00.043 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:17:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:17:01.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3755
2025-08-01 05:17:01.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2422
2025-08-01 05:17:01.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1526
2025-08-01 05:17:01.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2568
2025-08-01 05:17:01.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:17:01.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.257
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:17:01.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:17:01.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:17:01.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:17:01.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:17:02.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:17:03.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:17:03.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:17:04.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:17:04.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:17:05.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:17:06.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:17:06.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:17:06.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 05:17:06.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 05:17:06.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:17:06.650 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.84 ms, Average inference time: 8.28 ms

2025-08-01 05:17:06.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:17:06.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:17:06.929 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch216
2025-08-01 05:17:10.101 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 0.8, lr: 1.460e-03, size: 480, ETA: 2:30:15
2025-08-01 05:17:13.487 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 1.460e-03, size: 384, ETA: 2:30:10
2025-08-01 05:17:16.857 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.459e-03, size: 384, ETA: 2:30:06
2025-08-01 05:17:20.071 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.458e-03, size: 256, ETA: 2:30:01
2025-08-01 05:17:23.350 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.8, lr: 1.457e-03, size: 384, ETA: 2:29:57
2025-08-01 05:17:26.782 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 0.9, lr: 1.457e-03, size: 544, ETA: 2:29:53
2025-08-01 05:17:28.340 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:17:35.033 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:17:35.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:17:35.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4371
2025-08-01 05:17:35.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3897
2025-08-01 05:17:36.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2107
2025-08-01 05:17:36.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3458
2025-08-01 05:17:36.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:17:36.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:17:36.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 05:17:36.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-01 05:17:36.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-01 05:17:36.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.346
2025-08-01 05:17:36.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:17:36.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:17:36.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:17:36.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:17:36.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:17:36.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:17:36.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:17:36.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:17:36.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:17:36.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:17:36.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:17:37.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:17:37.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:17:37.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:17:38.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:17:38.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:17:39.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:17:39.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:17:39.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:17:39.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 05:17:39.447 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:17:39.453 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.81 ms, Average inference time: 8.38 ms

2025-08-01 05:17:39.454 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:17:39.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:17:39.660 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch217
2025-08-01 05:17:43.241 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.6, lr: 1.456e-03, size: 448, ETA: 2:29:47
2025-08-01 05:17:46.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.455e-03, size: 320, ETA: 2:29:43
2025-08-01 05:17:49.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 3.9, cls_loss: 0.7, lr: 1.454e-03, size: 544, ETA: 2:29:39
2025-08-01 05:17:53.215 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 1.3, lr: 1.453e-03, size: 544, ETA: 2:29:35
2025-08-01 05:17:56.820 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.7, lr: 1.453e-03, size: 576, ETA: 2:29:31
2025-08-01 05:18:00.447 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.452e-03, size: 576, ETA: 2:29:27
2025-08-01 05:18:02.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:18:08.805 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:18:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:18:09.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4660
2025-08-01 05:18:09.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4342
2025-08-01 05:18:09.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2900
2025-08-01 05:18:09.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3968
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.397
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:18:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:18:09.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:18:09.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:18:09.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:18:09.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:18:09.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:18:09.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:18:10.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:18:10.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:18:11.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:18:11.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:18:11.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:18:12.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:18:12.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:18:13.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:18:13.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:18:13.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 05:18:13.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 05:18:13.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:18:13.609 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.80 ms, Average inference time: 8.32 ms

2025-08-01 05:18:13.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:18:13.687 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:18:13.766 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch218
2025-08-01 05:18:16.963 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 1.451e-03, size: 416, ETA: 2:29:21
2025-08-01 05:18:20.337 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.450e-03, size: 576, ETA: 2:29:17
2025-08-01 05:18:23.821 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 1.449e-03, size: 384, ETA: 2:29:12
2025-08-01 05:18:27.211 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 0.8, lr: 1.449e-03, size: 512, ETA: 2:29:08
2025-08-01 05:18:30.609 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 1.448e-03, size: 320, ETA: 2:29:04
2025-08-01 05:18:33.819 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 1.447e-03, size: 256, ETA: 2:28:59
2025-08-01 05:18:35.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:18:42.299 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:18:42.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:18:43.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2168
2025-08-01 05:18:43.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2525
2025-08-01 05:18:43.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1296
2025-08-01 05:18:43.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1996
2025-08-01 05:18:43.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:18:43.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:18:43.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.252
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.130
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.200
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:18:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:18:43.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:18:43.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:18:43.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:18:44.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:18:44.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:18:45.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:18:45.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:18:45.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:18:46.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:18:46.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:18:46.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-08-01 05:18:46.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.20
2025-08-01 05:18:46.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:18:46.501 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.82 ms, Average inference time: 8.13 ms

2025-08-01 05:18:46.502 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:18:46.578 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:18:46.658 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch219
2025-08-01 05:18:50.106 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.446e-03, size: 384, ETA: 2:28:53
2025-08-01 05:18:53.555 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.445e-03, size: 384, ETA: 2:28:49
2025-08-01 05:18:57.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 1.445e-03, size: 352, ETA: 2:28:45
2025-08-01 05:19:00.373 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.8, lr: 1.444e-03, size: 384, ETA: 2:28:41
2025-08-01 05:19:03.745 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.443e-03, size: 512, ETA: 2:28:37
2025-08-01 05:19:07.269 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.6, lr: 1.442e-03, size: 256, ETA: 2:28:33
2025-08-01 05:19:08.800 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:19:15.299 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:19:15.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:19:15.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1535
2025-08-01 05:19:15.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1390
2025-08-01 05:19:15.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0532
2025-08-01 05:19:15.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1152
2025-08-01 05:19:15.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:19:15.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:19:15.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-08-01 05:19:15.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.139
2025-08-01 05:19:15.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.053
2025-08-01 05:19:15.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.115
2025-08-01 05:19:15.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:19:15.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:19:15.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:19:15.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:19:15.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:19:15.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:19:15.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:19:15.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:19:15.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:19:16.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:19:16.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:19:16.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:19:16.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:19:16.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:19:17.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:19:17.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:19:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:19:17.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:19:17.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-08-01 05:19:17.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.12
2025-08-01 05:19:17.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:19:17.613 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.57 ms, Average inference time: 8.17 ms

2025-08-01 05:19:17.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:19:17.702 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:19:17.779 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch220
2025-08-01 05:19:21.228 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.441e-03, size: 320, ETA: 2:28:27
2025-08-01 05:19:24.545 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 1.441e-03, size: 480, ETA: 2:28:23
2025-08-01 05:19:28.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.9, lr: 1.440e-03, size: 320, ETA: 2:28:19
2025-08-01 05:19:31.469 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.6, l1_loss: 1.6, conf_loss: 3.4, cls_loss: 0.9, lr: 1.439e-03, size: 576, ETA: 2:28:14
2025-08-01 05:19:34.988 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.7, lr: 1.438e-03, size: 448, ETA: 2:28:10
2025-08-01 05:19:38.287 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.438e-03, size: 416, ETA: 2:28:06
2025-08-01 05:19:39.801 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:19:46.525 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:19:47.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:19:47.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4624
2025-08-01 05:19:47.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3556
2025-08-01 05:19:47.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2038
2025-08-01 05:19:47.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3406
2025-08-01 05:19:47.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:19:47.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:19:47.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-01 05:19:47.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-01 05:19:47.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.204
2025-08-01 05:19:47.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.341
2025-08-01 05:19:47.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:19:47.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:19:47.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:19:47.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:19:47.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:19:47.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:19:47.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:19:47.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:19:47.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:19:48.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:19:48.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:19:49.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:19:50.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:19:50.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:19:51.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:19:51.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:19:52.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:19:52.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:19:52.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:19:52.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:19:52.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:19:52.844 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.82 ms, Average inference time: 8.31 ms

2025-08-01 05:19:52.845 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:19:52.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:19:53.004 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch221
2025-08-01 05:19:56.290 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.6, lr: 1.437e-03, size: 544, ETA: 2:28:00
2025-08-01 05:19:59.482 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 1.436e-03, size: 288, ETA: 2:27:55
2025-08-01 05:20:03.072 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.435e-03, size: 576, ETA: 2:27:51
2025-08-01 05:20:06.417 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.8, lr: 1.434e-03, size: 512, ETA: 2:27:47
2025-08-01 05:20:09.641 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, 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: 1.434e-03, size: 416, ETA: 2:27:43
2025-08-01 05:20:12.955 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.164s, 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: 1.433e-03, size: 288, ETA: 2:27:39
2025-08-01 05:20:14.514 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:20:21.155 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:20:21.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:20:21.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4514
2025-08-01 05:20:21.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3887
2025-08-01 05:20:21.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1678
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3359
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.168
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:20:21.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:20:21.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:20:21.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:20:21.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:20:21.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:20:21.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:20:21.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:20:21.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:20:22.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:20:22.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:20:23.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:20:23.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:20:23.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:20:24.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:20:24.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:20:24.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:20:25.153 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:20:25.154 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 05:20:25.154 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:20:25.154 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:20:25.160 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.76 ms, Average inference time: 8.23 ms

2025-08-01 05:20:25.161 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:20:25.246 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:20:25.323 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch222
2025-08-01 05:20:28.506 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.8, lr: 1.432e-03, size: 416, ETA: 2:27:32
2025-08-01 05:20:31.818 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.431e-03, size: 448, ETA: 2:27:28
2025-08-01 05:20:35.337 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 3.4, cls_loss: 1.2, lr: 1.430e-03, size: 576, ETA: 2:27:24
2025-08-01 05:20:38.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.430e-03, size: 352, ETA: 2:27:20
2025-08-01 05:20:42.042 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 1.429e-03, size: 576, ETA: 2:27:16
2025-08-01 05:20:45.418 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.428e-03, size: 480, ETA: 2:27:12
2025-08-01 05:20:46.870 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:20:53.676 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:20:54.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:20:54.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4182
2025-08-01 05:20:55.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3936
2025-08-01 05:20:55.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2171
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3430
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.343
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:20:55.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:20:55.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:20:55.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:20:55.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:20:55.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:20:55.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:20:55.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:20:55.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:20:55.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:20:56.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:20:56.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:20:57.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:20:58.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:20:58.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:20:59.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:20:59.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:21:00.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:21:00.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:21:00.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:21:00.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:21:00.467 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.83 ms, Average inference time: 8.29 ms

2025-08-01 05:21:00.468 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:21:00.628 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:21:00.713 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch223
2025-08-01 05:21:03.922 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.427e-03, size: 512, ETA: 2:27:05
2025-08-01 05:21:07.190 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.7, lr: 1.426e-03, size: 416, ETA: 2:27:01
2025-08-01 05:21:10.582 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.8, lr: 1.425e-03, size: 288, ETA: 2:26:57
2025-08-01 05:21:14.074 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 0.5, lr: 1.425e-03, size: 352, ETA: 2:26:53
2025-08-01 05:21:17.296 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, 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: 1.424e-03, size: 256, ETA: 2:26:48
2025-08-01 05:21:20.613 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.6, lr: 1.423e-03, size: 320, ETA: 2:26:44
2025-08-01 05:21:22.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:21:28.741 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:21:29.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:21:29.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4579
2025-08-01 05:21:29.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4002
2025-08-01 05:21:29.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2560
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3713
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.256
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:21:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:21:29.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:21:29.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:21:29.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:21:29.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:21:29.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:21:29.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:21:29.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:21:29.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:21:30.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:21:30.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:21:30.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:21:30.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:21:31.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:21:31.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:21:32.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:21:32.321 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:21:32.322 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:21:32.322 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 05:21:32.322 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:21:32.331 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.75 ms, Average inference time: 8.11 ms

2025-08-01 05:21:32.332 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:21:32.505 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:21:32.603 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch224
2025-08-01 05:21:35.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.422e-03, size: 448, ETA: 2:26:37
2025-08-01 05:21:38.927 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 1.421e-03, size: 352, ETA: 2:26:33
2025-08-01 05:21:42.079 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 10.7, iou_loss: 3.4, l1_loss: 1.6, conf_loss: 4.9, cls_loss: 0.9, lr: 1.421e-03, size: 352, ETA: 2:26:29
2025-08-01 05:21:45.625 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.420e-03, size: 320, ETA: 2:26:25
2025-08-01 05:21:48.905 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.419e-03, size: 416, ETA: 2:26:20
2025-08-01 05:21:52.613 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 1.418e-03, size: 480, ETA: 2:26:17
2025-08-01 05:21:54.069 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:22:00.680 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:22:01.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:22:02.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4565
2025-08-01 05:22:02.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3519
2025-08-01 05:22:02.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2056
2025-08-01 05:22:02.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3380
2025-08-01 05:22:02.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:22:02.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:22:02.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 05:22:02.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.206
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.338
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:22:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:22:02.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:22:03.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:22:04.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:22:05.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:22:06.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:22:06.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:22:07.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:22:08.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:22:09.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:22:10.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:22:10.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:22:10.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:22:10.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:22:10.648 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.82 ms, Average inference time: 8.29 ms

2025-08-01 05:22:10.649 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:22:10.746 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:22:10.891 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch225
2025-08-01 05:22:14.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.417e-03, size: 544, ETA: 2:26:10
2025-08-01 05:22:17.415 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.9, lr: 1.417e-03, size: 480, ETA: 2:26:06
2025-08-01 05:22:20.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.8, lr: 1.416e-03, size: 384, ETA: 2:26:02
2025-08-01 05:22:24.013 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.415e-03, size: 416, ETA: 2:25:58
2025-08-01 05:22:27.301 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.414e-03, size: 352, ETA: 2:25:53
2025-08-01 05:22:30.546 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.7, lr: 1.414e-03, size: 288, ETA: 2:25:49
2025-08-01 05:22:31.953 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:22:38.796 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:22:40.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:22:41.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4624
2025-08-01 05:22:41.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3931
2025-08-01 05:22:41.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2431
2025-08-01 05:22:41.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3662
2025-08-01 05:22:41.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.243
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.366
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:22:41.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:22:41.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:22:41.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:22:42.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:22:43.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:22:45.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:22:46.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:22:47.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:22:49.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:22:50.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:22:51.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:22:52.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:22:52.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:22:52.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 05:22:52.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:22:52.894 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.86 ms, Average inference time: 8.42 ms

2025-08-01 05:22:52.896 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:22:52.977 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:22:53.061 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch226
2025-08-01 05:22:56.215 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.412e-03, size: 320, ETA: 2:25:43
2025-08-01 05:22:59.600 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 3.4, cls_loss: 0.8, lr: 1.412e-03, size: 416, ETA: 2:25:38
2025-08-01 05:23:02.936 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.411e-03, size: 512, ETA: 2:25:34
2025-08-01 05:23:06.430 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, 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: 1.410e-03, size: 576, ETA: 2:25:30
2025-08-01 05:23:09.931 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, 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: 1.409e-03, size: 448, ETA: 2:25:26
2025-08-01 05:23:13.403 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.003s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 1.409e-03, size: 512, ETA: 2:25:22
2025-08-01 05:23:14.866 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:23:21.477 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:23:21.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:23:22.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4000
2025-08-01 05:23:22.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3181
2025-08-01 05:23:22.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1712
2025-08-01 05:23:22.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2964
2025-08-01 05:23:22.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:23:22.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:23:22.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 05:23:22.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-01 05:23:22.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.171
2025-08-01 05:23:22.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.296
2025-08-01 05:23:22.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:23:22.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:23:22.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:23:22.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:23:22.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:23:22.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:23:22.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:23:22.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:23:22.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:23:22.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:23:23.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:23:23.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:23:23.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:23:24.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:23:24.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:23:25.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:23:25.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:23:25.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:23:25.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:23:25.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 05:23:25.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:23:25.733 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.82 ms, Average inference time: 8.25 ms

2025-08-01 05:23:25.734 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:23:25.861 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:23:25.946 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch227
2025-08-01 05:23:29.149 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.9, lr: 1.408e-03, size: 288, ETA: 2:25:16
2025-08-01 05:23:32.335 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 9.3, iou_loss: 3.3, l1_loss: 1.4, conf_loss: 3.7, cls_loss: 0.9, lr: 1.407e-03, size: 384, ETA: 2:25:11
2025-08-01 05:23:35.697 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.7, lr: 1.406e-03, size: 320, ETA: 2:25:07
2025-08-01 05:23:38.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.405e-03, size: 320, ETA: 2:25:03
2025-08-01 05:23:42.376 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, 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: 1.405e-03, size: 256, ETA: 2:24:59
2025-08-01 05:23:45.853 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 1.4, lr: 1.404e-03, size: 352, ETA: 2:24:55
2025-08-01 05:23:47.304 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:23:54.026 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:23:54.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:23:55.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4213
2025-08-01 05:23:55.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3795
2025-08-01 05:23:55.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1771
2025-08-01 05:23:55.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3259
2025-08-01 05:23:55.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:23:55.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:23:55.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-08-01 05:23:55.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.177
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.326
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:23:55.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:23:55.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:23:56.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:23:57.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:23:57.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:23:58.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:23:59.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:24:00.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:24:00.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:24:01.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:24:02.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:24:02.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:24:02.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 05:24:02.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:24:02.406 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.86 ms, Average inference time: 8.34 ms

2025-08-01 05:24:02.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:24:02.489 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:24:02.572 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch228
2025-08-01 05:24:06.038 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.403e-03, size: 416, ETA: 2:24:49
2025-08-01 05:24:09.278 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.402e-03, size: 512, ETA: 2:24:45
2025-08-01 05:24:12.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.9, lr: 1.401e-03, size: 576, ETA: 2:24:41
2025-08-01 05:24:16.312 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 3.6, cls_loss: 0.9, lr: 1.400e-03, size: 576, ETA: 2:24:37
2025-08-01 05:24:19.804 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 8.7, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.9, lr: 1.400e-03, size: 544, ETA: 2:24:33
2025-08-01 05:24:23.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.1Gb, iter_time: 0.173s, data_time: 0.004s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 0.7, lr: 1.399e-03, size: 320, ETA: 2:24:29
2025-08-01 05:24:24.815 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:24:31.451 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:24:32.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:24:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4240
2025-08-01 05:24:33.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3391
2025-08-01 05:24:33.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1623
2025-08-01 05:24:33.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3085
2025-08-01 05:24:33.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:24:33.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:24:33.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 05:24:33.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-08-01 05:24:33.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.162
2025-08-01 05:24:33.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.308
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:24:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:24:34.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:24:35.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:24:36.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:24:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:24:38.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:24:39.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:24:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:24:40.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:24:41.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:24:41.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 05:24:41.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 05:24:41.785 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:24:41.795 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.84 ms, Average inference time: 8.19 ms

2025-08-01 05:24:41.796 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:24:41.935 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:24:42.039 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch229
2025-08-01 05:24:45.128 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 1.398e-03, size: 256, ETA: 2:24:23
2025-08-01 05:24:48.553 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.397e-03, size: 416, ETA: 2:24:19
2025-08-01 05:24:52.083 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.8, lr: 1.396e-03, size: 288, ETA: 2:24:15
2025-08-01 05:24:55.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.8, lr: 1.396e-03, size: 256, ETA: 2:24:11
2025-08-01 05:24:58.718 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 10.6, iou_loss: 3.2, l1_loss: 1.6, conf_loss: 5.0, cls_loss: 0.9, lr: 1.395e-03, size: 448, ETA: 2:24:06
2025-08-01 05:25:02.155 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.006s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.394e-03, size: 512, ETA: 2:24:02
2025-08-01 05:25:03.625 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:25:10.326 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:25:11.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:25:11.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4277
2025-08-01 05:25:11.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3764
2025-08-01 05:25:11.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2274
2025-08-01 05:25:11.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3438
2025-08-01 05:25:11.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:25:11.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:25:11.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-01 05:25:11.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-01 05:25:11.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-08-01 05:25:11.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:25:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:25:12.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:25:13.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:25:13.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:25:14.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:25:14.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:25:15.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:25:16.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:25:16.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:25:17.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:25:17.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:25:17.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:25:17.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:25:17.489 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.81 ms, Average inference time: 8.27 ms

2025-08-01 05:25:17.490 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:25:17.608 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:25:17.716 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch230
2025-08-01 05:25:21.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.393e-03, size: 384, ETA: 2:23:56
2025-08-01 05:25:24.228 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.8, lr: 1.392e-03, size: 416, ETA: 2:23:52
2025-08-01 05:25:27.610 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.7, lr: 1.391e-03, size: 320, ETA: 2:23:48
2025-08-01 05:25:30.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.0, l1_loss: 1.6, conf_loss: 3.8, cls_loss: 0.8, lr: 1.391e-03, size: 448, ETA: 2:23:44
2025-08-01 05:25:34.339 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.390e-03, size: 320, ETA: 2:23:40
2025-08-01 05:25:37.679 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 1.389e-03, size: 544, ETA: 2:23:35
2025-08-01 05:25:39.260 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:25:46.014 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:25:46.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:25:47.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4449
2025-08-01 05:25:47.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3229
2025-08-01 05:25:47.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1986
2025-08-01 05:25:47.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3221
2025-08-01 05:25:47.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:25:47.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:25:47.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.199
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:25:47.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:25:48.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:25:48.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:25:49.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:25:50.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:25:50.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:25:51.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:25:51.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:25:52.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:25:53.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:25:53.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:25:53.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 05:25:53.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:25:53.194 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.83 ms, Average inference time: 8.29 ms

2025-08-01 05:25:53.195 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:25:53.273 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:25:53.354 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch231
2025-08-01 05:25:56.515 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.388e-03, size: 352, ETA: 2:23:29
2025-08-01 05:25:59.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.387e-03, size: 288, ETA: 2:23:25
2025-08-01 05:26:03.083 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 1.387e-03, size: 448, ETA: 2:23:21
2025-08-01 05:26:06.411 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, 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: 1.386e-03, size: 320, ETA: 2:23:17
2025-08-01 05:26:09.980 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.8, lr: 1.385e-03, size: 320, ETA: 2:23:13
2025-08-01 05:26:13.423 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 8.7, iou_loss: 2.5, l1_loss: 1.6, conf_loss: 3.9, cls_loss: 0.6, lr: 1.384e-03, size: 544, ETA: 2:23:09
2025-08-01 05:26:15.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:26:21.983 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:26:22.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:26:22.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2765
2025-08-01 05:26:22.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3121
2025-08-01 05:26:22.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1154
2025-08-01 05:26:22.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2346
2025-08-01 05:26:22.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:26:22.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:26:22.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-08-01 05:26:22.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.115
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.235
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:26:22.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:26:22.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:26:22.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:26:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:26:23.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:26:23.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:26:23.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:26:24.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:26:24.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:26:24.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:26:24.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:26:25.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:26:25.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 05:26:25.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-08-01 05:26:25.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:26:25.205 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.73 ms, Average inference time: 8.14 ms

2025-08-01 05:26:25.206 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:26:25.327 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:26:25.402 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch232
2025-08-01 05:26:28.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.383e-03, size: 480, ETA: 2:23:03
2025-08-01 05:26:32.044 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.7, lr: 1.382e-03, size: 480, ETA: 2:22:59
2025-08-01 05:26:35.409 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.5, lr: 1.382e-03, size: 512, ETA: 2:22:55
2025-08-01 05:26:38.614 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.381e-03, size: 384, ETA: 2:22:51
2025-08-01 05:26:42.100 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.5, cls_loss: 0.7, lr: 1.380e-03, size: 320, ETA: 2:22:47
2025-08-01 05:26:45.599 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 10.0, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 4.9, cls_loss: 0.7, lr: 1.379e-03, size: 544, ETA: 2:22:43
2025-08-01 05:26:47.147 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:26:53.965 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:26:55.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:26:57.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2583
2025-08-01 05:26:57.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3170
2025-08-01 05:26:57.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1523
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2425
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.317
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.152
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.243
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:26:57.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:26:57.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:26:57.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:26:57.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:26:57.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:26:57.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:26:57.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:26:57.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:26:59.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:27:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:27:02.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:27:04.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:27:06.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:27:07.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:27:09.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:27:11.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:27:13.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:27:13.050 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 05:27:13.050 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 05:27:13.050 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:27:13.086 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.86 ms, Average inference time: 8.31 ms

2025-08-01 05:27:13.087 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:27:13.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:27:13.289 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch233
2025-08-01 05:27:16.597 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 1.378e-03, size: 384, ETA: 2:22:37
2025-08-01 05:27:20.003 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 1.2, lr: 1.377e-03, size: 256, ETA: 2:22:33
2025-08-01 05:27:23.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 1.1, lr: 1.377e-03, size: 384, ETA: 2:22:29
2025-08-01 05:27:26.719 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.5, lr: 1.376e-03, size: 416, ETA: 2:22:25
2025-08-01 05:27:30.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.375e-03, size: 544, ETA: 2:22:20
2025-08-01 05:27:33.565 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.374e-03, size: 320, ETA: 2:22:16
2025-08-01 05:27:35.100 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:27:42.029 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:27:43.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:27:45.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4071
2025-08-01 05:27:45.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3570
2025-08-01 05:27:45.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1960
2025-08-01 05:27:45.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3200
2025-08-01 05:27:45.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:27:45.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:27:45.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 05:27:45.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-08-01 05:27:45.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.196
2025-08-01 05:27:45.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.320
2025-08-01 05:27:45.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:27:45.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:27:45.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:27:45.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:27:45.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:27:45.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:27:45.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:27:45.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:27:45.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:27:47.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:27:48.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:27:50.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:27:52.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:27:53.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:27:55.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:27:57.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:27:59.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:28:00.649 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:28:00.649 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:28:00.649 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 05:28:00.649 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:28:00.675 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.85 ms, Average inference time: 8.30 ms

2025-08-01 05:28:00.677 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:28:00.760 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:28:00.843 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch234
2025-08-01 05:28:03.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.8, l1_loss: 1.7, conf_loss: 3.1, cls_loss: 0.7, lr: 1.373e-03, size: 384, ETA: 2:22:10
2025-08-01 05:28:07.218 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.373e-03, size: 256, ETA: 2:22:06
2025-08-01 05:28:10.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.372e-03, size: 320, ETA: 2:22:01
2025-08-01 05:28:14.073 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 1.371e-03, size: 512, ETA: 2:21:58
2025-08-01 05:28:17.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.370e-03, size: 448, ETA: 2:21:54
2025-08-01 05:28:20.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 0.8, lr: 1.369e-03, size: 544, ETA: 2:21:50
2025-08-01 05:28:22.495 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:28:29.228 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:28:29.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:28:30.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3296
2025-08-01 05:28:30.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3360
2025-08-01 05:28:30.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1001
2025-08-01 05:28:30.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2552
2025-08-01 05:28:30.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:28:30.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:28:30.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.330
2025-08-01 05:28:30.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-08-01 05:28:30.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.100
2025-08-01 05:28:30.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.255
2025-08-01 05:28:30.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:28:30.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:28:30.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:28:30.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:28:30.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:28:30.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:28:30.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:28:30.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:28:30.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:28:31.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:28:31.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:28:32.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:28:32.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:28:33.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:28:33.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:28:34.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:28:34.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:28:35.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:28:35.293 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 05:28:35.293 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 05:28:35.293 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:28:35.300 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.63 ms, Average NMS time: 0.83 ms, Average inference time: 8.46 ms

2025-08-01 05:28:35.301 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:28:35.383 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:28:35.463 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch235
2025-08-01 05:28:38.718 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.368e-03, size: 448, ETA: 2:21:44
2025-08-01 05:28:42.051 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.368e-03, size: 352, ETA: 2:21:40
2025-08-01 05:28:45.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, 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: 1.367e-03, size: 384, ETA: 2:21:35
2025-08-01 05:28:48.570 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.6, lr: 1.366e-03, size: 480, ETA: 2:21:31
2025-08-01 05:28:51.824 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.365e-03, size: 384, ETA: 2:21:27
2025-08-01 05:28:55.198 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.8, lr: 1.365e-03, size: 544, ETA: 2:21:23
2025-08-01 05:28:56.726 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:29:03.416 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:29:03.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4223
2025-08-01 05:29:04.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2422
2025-08-01 05:29:04.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1859
2025-08-01 05:29:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2835
2025-08-01 05:29:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:29:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.283
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:29:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:29:04.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:29:04.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:29:05.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:29:05.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:29:06.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:29:06.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:29:07.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:29:07.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:29:08.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:29:08.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:29:08.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 05:29:08.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 05:29:08.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:29:08.551 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.76 ms, Average inference time: 8.29 ms

2025-08-01 05:29:08.552 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:29:08.665 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:29:08.781 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch236
2025-08-01 05:29:12.020 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 1.363e-03, size: 416, ETA: 2:21:17
2025-08-01 05:29:15.494 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.363e-03, size: 352, ETA: 2:21:13
2025-08-01 05:29:18.673 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, 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: 1.362e-03, size: 288, ETA: 2:21:08
2025-08-01 05:29:22.237 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.361e-03, size: 448, ETA: 2:21:05
2025-08-01 05:29:25.571 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 1.360e-03, size: 448, ETA: 2:21:01
2025-08-01 05:29:28.732 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 1.360e-03, size: 352, ETA: 2:20:56
2025-08-01 05:29:30.281 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:29:36.891 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:29:37.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:29:38.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4143
2025-08-01 05:29:38.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3705
2025-08-01 05:29:38.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1984
2025-08-01 05:29:38.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3277
2025-08-01 05:29:38.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.328
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:29:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:29:38.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:29:38.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:29:38.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:29:38.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:29:38.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:29:39.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:29:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:29:40.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:29:41.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:29:41.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:29:42.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:29:43.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:29:43.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:29:43.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:29:43.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 05:29:43.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:29:43.665 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.82 ms, Average inference time: 8.22 ms

2025-08-01 05:29:43.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:29:43.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:29:43.834 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch237
2025-08-01 05:29:47.049 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.8, lr: 1.358e-03, size: 416, ETA: 2:20:50
2025-08-01 05:29:50.465 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.358e-03, size: 288, ETA: 2:20:46
2025-08-01 05:29:53.860 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 1.1, lr: 1.357e-03, size: 256, ETA: 2:20:42
2025-08-01 05:29:57.388 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 9.5, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 4.1, cls_loss: 1.0, lr: 1.356e-03, size: 544, ETA: 2:20:38
2025-08-01 05:30:00.831 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 4.1, cls_loss: 0.8, lr: 1.355e-03, size: 448, ETA: 2:20:34
2025-08-01 05:30:04.234 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.9, lr: 1.355e-03, size: 256, ETA: 2:20:30
2025-08-01 05:30:05.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:30:12.436 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:30:14.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:30:15.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5113
2025-08-01 05:30:15.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4474
2025-08-01 05:30:15.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2565
2025-08-01 05:30:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4051
2025-08-01 05:30:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:30:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:30:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.256
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.405
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:30:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:30:15.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:30:17.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:30:19.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:30:20.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:30:22.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:30:23.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:30:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:30:26.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:30:28.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:30:30.050 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:30:30.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 05:30:30.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 05:30:30.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:30:30.065 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.84 ms, Average inference time: 8.29 ms

2025-08-01 05:30:30.067 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:30:30.185 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:30:30.334 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch238
2025-08-01 05:30:33.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 1.353e-03, size: 384, ETA: 2:20:24
2025-08-01 05:30:37.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.187s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 1.353e-03, size: 448, ETA: 2:20:20
2025-08-01 05:30:40.652 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 1.352e-03, size: 512, ETA: 2:20:16
2025-08-01 05:30:44.129 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 1.351e-03, size: 416, ETA: 2:20:12
2025-08-01 05:30:47.530 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 9.1, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.8, lr: 1.350e-03, size: 288, ETA: 2:20:08
2025-08-01 05:30:50.854 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 10.7, iou_loss: 3.1, l1_loss: 1.6, conf_loss: 5.1, cls_loss: 0.9, lr: 1.350e-03, size: 480, ETA: 2:20:04
2025-08-01 05:30:52.345 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:30:59.194 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:30:59.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:30:59.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3533
2025-08-01 05:30:59.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3137
2025-08-01 05:31:00.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1621
2025-08-01 05:31:00.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2764
2025-08-01 05:31:00.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:31:00.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:31:00.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-08-01 05:31:00.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-01 05:31:00.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.162
2025-08-01 05:31:00.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.276
2025-08-01 05:31:00.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:31:00.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:31:00.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:31:00.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:31:00.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:31:00.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:31:00.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:31:00.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:31:00.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:31:00.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:31:00.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:31:00.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:31:01.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:31:01.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:31:01.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:31:02.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:31:02.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:31:02.830 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:31:02.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:31:02.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 05:31:02.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:31:02.840 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.84 ms, Average inference time: 8.36 ms

2025-08-01 05:31:02.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:31:02.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:31:03.037 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch239
2025-08-01 05:31:06.367 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 1.348e-03, size: 480, ETA: 2:19:58
2025-08-01 05:31:09.735 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.7, lr: 1.348e-03, size: 544, ETA: 2:19:54
2025-08-01 05:31:13.205 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.347e-03, size: 416, ETA: 2:19:50
2025-08-01 05:31:16.377 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.346e-03, size: 416, ETA: 2:19:46
2025-08-01 05:31:19.786 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, 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: 1.345e-03, size: 576, ETA: 2:19:42
2025-08-01 05:31:23.196 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 1.345e-03, size: 320, ETA: 2:19:38
2025-08-01 05:31:24.635 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:31:31.378 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:31:32.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:31:33.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5055
2025-08-01 05:31:33.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4438
2025-08-01 05:31:33.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2582
2025-08-01 05:31:33.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4025
2025-08-01 05:31:33.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:31:33.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:31:33.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.402
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:31:33.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:31:34.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:31:34.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:31:35.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:31:36.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:31:37.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:31:37.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:31:38.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:31:39.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:31:39.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:31:39.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 05:31:39.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 05:31:39.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:31:39.897 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.83 ms, Average inference time: 8.26 ms

2025-08-01 05:31:39.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:31:39.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:31:40.063 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch240
2025-08-01 05:31:43.253 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.343e-03, size: 416, ETA: 2:19:32
2025-08-01 05:31:46.730 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 1.343e-03, size: 256, ETA: 2:19:28
2025-08-01 05:31:50.064 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.8, lr: 1.342e-03, size: 288, ETA: 2:19:24
2025-08-01 05:31:53.384 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.341e-03, size: 320, ETA: 2:19:20
2025-08-01 05:31:56.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.340e-03, size: 384, ETA: 2:19:15
2025-08-01 05:32:00.115 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 2.8, cls_loss: 0.7, lr: 1.340e-03, size: 480, ETA: 2:19:11
2025-08-01 05:32:01.613 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:32:08.292 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:32:09.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:32:09.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4473
2025-08-01 05:32:09.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3533
2025-08-01 05:32:09.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2511
2025-08-01 05:32:09.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3506
2025-08-01 05:32:09.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:32:09.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:32:09.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 05:32:09.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-08-01 05:32:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.251
2025-08-01 05:32:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.351
2025-08-01 05:32:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:32:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:32:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:32:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:32:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:32:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:32:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:32:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:32:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:32:10.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:32:10.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:32:11.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:32:11.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:32:12.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:32:12.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:32:13.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:32:14.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:32:14.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:32:14.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:32:14.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 05:32:14.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:32:14.643 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.82 ms, Average inference time: 8.30 ms

2025-08-01 05:32:14.645 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:32:14.722 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:32:14.801 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch241
2025-08-01 05:32:18.021 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, 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: 1.338e-03, size: 384, ETA: 2:19:05
2025-08-01 05:32:21.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.338e-03, size: 352, ETA: 2:19:01
2025-08-01 05:32:24.726 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.337e-03, size: 384, ETA: 2:18:57
2025-08-01 05:32:28.313 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 1.336e-03, size: 576, ETA: 2:18:53
2025-08-01 05:32:31.610 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.335e-03, size: 384, ETA: 2:18:49
2025-08-01 05:32:34.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.8, lr: 1.335e-03, size: 320, ETA: 2:18:45
2025-08-01 05:32:36.387 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:32:43.038 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:32:43.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:32:44.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3818
2025-08-01 05:32:44.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3453
2025-08-01 05:32:44.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1358
2025-08-01 05:32:44.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2876
2025-08-01 05:32:44.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:32:44.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:32:44.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-01 05:32:44.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-01 05:32:44.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.136
2025-08-01 05:32:44.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.288
2025-08-01 05:32:44.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:32:44.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:32:44.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:32:44.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:32:44.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:32:44.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:32:44.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:32:44.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:32:44.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:32:44.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:32:45.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:32:45.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:32:46.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:32:46.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:32:47.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:32:47.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:32:48.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:32:48.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:32:48.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:32:48.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 05:32:48.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:32:48.955 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.82 ms, Average inference time: 8.24 ms

2025-08-01 05:32:48.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:32:49.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:32:49.115 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch242
2025-08-01 05:32:52.395 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.7, lr: 1.333e-03, size: 416, ETA: 2:18:39
2025-08-01 05:32:55.665 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 1.6, conf_loss: 2.5, cls_loss: 0.7, lr: 1.333e-03, size: 576, ETA: 2:18:35
2025-08-01 05:32:59.093 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.9, lr: 1.332e-03, size: 384, ETA: 2:18:31
2025-08-01 05:33:02.416 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 1.0, lr: 1.331e-03, size: 256, ETA: 2:18:27
2025-08-01 05:33:05.779 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.330e-03, size: 512, ETA: 2:18:23
2025-08-01 05:33:08.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.330e-03, size: 256, ETA: 2:18:19
2025-08-01 05:33:10.470 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:33:17.344 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:33:18.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:33:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2012
2025-08-01 05:33:19.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2941
2025-08-01 05:33:19.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2011
2025-08-01 05:33:19.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2321
2025-08-01 05:33:19.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:33:19.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:33:19.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-08-01 05:33:19.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-08-01 05:33:19.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-08-01 05:33:19.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.232
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:33:19.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:33:20.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:33:21.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:33:22.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:33:23.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:33:24.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:33:25.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:33:26.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:33:28.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:33:29.012 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:33:29.012 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-08-01 05:33:29.012 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-08-01 05:33:29.012 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:33:29.020 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.87 ms, Average inference time: 8.44 ms

2025-08-01 05:33:29.021 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:33:29.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:33:29.208 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch243
2025-08-01 05:33:32.573 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.9, lr: 1.328e-03, size: 512, ETA: 2:18:13
2025-08-01 05:33:35.948 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.7, lr: 1.328e-03, size: 416, ETA: 2:18:09
2025-08-01 05:33:39.260 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.6, lr: 1.327e-03, size: 256, ETA: 2:18:05
2025-08-01 05:33:42.401 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 1.326e-03, size: 384, ETA: 2:18:00
2025-08-01 05:33:45.727 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.325e-03, size: 256, ETA: 2:17:56
2025-08-01 05:33:48.852 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 1.1, lr: 1.325e-03, size: 256, ETA: 2:17:52
2025-08-01 05:33:50.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:33:57.039 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:33:57.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:33:58.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3388
2025-08-01 05:33:58.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3453
2025-08-01 05:33:58.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2652
2025-08-01 05:33:58.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3164
2025-08-01 05:33:58.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:33:58.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:33:58.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-08-01 05:33:58.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-01 05:33:58.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.265
2025-08-01 05:33:58.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.316
2025-08-01 05:33:58.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:33:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:33:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:33:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:33:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:33:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:33:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:33:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:33:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:33:58.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:33:59.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:34:00.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:34:00.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:34:01.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:34:01.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:34:02.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:34:02.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:34:03.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:34:03.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:34:03.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 05:34:03.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:34:03.290 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.83 ms, Average inference time: 8.20 ms

2025-08-01 05:34:03.291 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:34:03.471 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:34:03.563 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch244
2025-08-01 05:34:06.997 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.323e-03, size: 576, ETA: 2:17:46
2025-08-01 05:34:10.500 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.323e-03, size: 480, ETA: 2:17:42
2025-08-01 05:34:13.779 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 2.5, cls_loss: 0.7, lr: 1.322e-03, size: 512, ETA: 2:17:38
2025-08-01 05:34:17.234 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.321e-03, size: 416, ETA: 2:17:34
2025-08-01 05:34:20.899 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 1.320e-03, size: 544, ETA: 2:17:30
2025-08-01 05:34:24.466 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.8, lr: 1.319e-03, size: 384, ETA: 2:17:27
2025-08-01 05:34:25.877 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:34:32.651 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:34:34.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:34:35.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4578
2025-08-01 05:34:35.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3678
2025-08-01 05:34:35.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2422
2025-08-01 05:34:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3559
2025-08-01 05:34:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:34:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:34:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-01 05:34:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-01 05:34:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:34:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:34:36.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:34:37.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:34:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:34:40.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:34:41.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:34:42.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:34:43.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:34:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:34:46.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:34:46.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:34:46.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 05:34:46.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:34:46.070 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.88 ms, Average inference time: 8.29 ms

2025-08-01 05:34:46.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:34:46.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:34:46.255 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch245
2025-08-01 05:34:49.695 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.8, lr: 1.318e-03, size: 288, ETA: 2:17:21
2025-08-01 05:34:53.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.6, lr: 1.318e-03, size: 256, ETA: 2:17:17
2025-08-01 05:34:56.373 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.6, lr: 1.317e-03, size: 288, ETA: 2:17:13
2025-08-01 05:34:59.753 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.316e-03, size: 480, ETA: 2:17:09
2025-08-01 05:35:03.153 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, 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: 1.315e-03, size: 512, ETA: 2:17:05
2025-08-01 05:35:06.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, 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: 1.314e-03, size: 448, ETA: 2:17:01
2025-08-01 05:35:07.948 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:35:14.687 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:35:15.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:35:15.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4321
2025-08-01 05:35:15.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3277
2025-08-01 05:35:15.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2230
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3276
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.328
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:35:15.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:35:15.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:35:15.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:35:15.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:35:15.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:35:15.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:35:15.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:35:15.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:35:16.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:35:16.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:35:16.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:35:17.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:35:17.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:35:18.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:35:18.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:35:18.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:35:19.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:35:19.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:35:19.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 05:35:19.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:35:19.346 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.79 ms, Average inference time: 8.38 ms

2025-08-01 05:35:19.347 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:35:19.425 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:35:19.551 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch246
2025-08-01 05:35:22.672 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.313e-03, size: 352, ETA: 2:16:54
2025-08-01 05:35:26.057 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.7, lr: 1.313e-03, size: 576, ETA: 2:16:51
2025-08-01 05:35:29.587 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.7, lr: 1.312e-03, size: 384, ETA: 2:16:47
2025-08-01 05:35:32.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, 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: 1.311e-03, size: 416, ETA: 2:16:43
2025-08-01 05:35:36.262 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 0.6, lr: 1.310e-03, size: 384, ETA: 2:16:39
2025-08-01 05:35:39.663 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.8, lr: 1.309e-03, size: 320, ETA: 2:16:35
2025-08-01 05:35:41.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:35:47.861 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:35:48.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:35:49.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4511
2025-08-01 05:35:49.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3828
2025-08-01 05:35:49.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2320
2025-08-01 05:35:49.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3553
2025-08-01 05:35:49.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.355
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:35:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:35:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:35:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:35:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:35:49.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:35:50.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:35:51.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:35:51.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:35:52.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:35:53.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:35:53.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:35:54.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:35:55.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:35:55.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:35:55.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 05:35:55.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:35:55.122 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.83 ms, Average inference time: 8.27 ms

2025-08-01 05:35:55.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:35:55.208 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:35:55.288 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch247
2025-08-01 05:35:58.428 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.8, lr: 1.308e-03, size: 320, ETA: 2:16:29
2025-08-01 05:36:02.025 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.8, lr: 1.307e-03, size: 256, ETA: 2:16:25
2025-08-01 05:36:05.476 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 9.7, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 4.8, cls_loss: 0.8, lr: 1.307e-03, size: 320, ETA: 2:16:21
2025-08-01 05:36:08.883 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 1.306e-03, size: 480, ETA: 2:16:17
2025-08-01 05:36:12.128 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.6, lr: 1.305e-03, size: 352, ETA: 2:16:13
2025-08-01 05:36:15.686 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 1.304e-03, size: 448, ETA: 2:16:09
2025-08-01 05:36:17.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:36:23.774 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:36:24.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:36:25.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4403
2025-08-01 05:36:25.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3948
2025-08-01 05:36:25.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1948
2025-08-01 05:36:25.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3433
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.343
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:36:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:36:25.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:36:25.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:36:25.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:36:25.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:36:25.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:36:25.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:36:26.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:36:27.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:36:27.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:36:28.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:36:28.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:36:29.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:36:30.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:36:30.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:36:30.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:36:30.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:36:30.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:36:30.830 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.82 ms, Average inference time: 8.15 ms

2025-08-01 05:36:30.831 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:36:30.908 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:36:30.989 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch248
2025-08-01 05:36:34.373 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.303e-03, size: 576, ETA: 2:16:03
2025-08-01 05:36:37.956 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.302e-03, size: 544, ETA: 2:15:59
2025-08-01 05:36:41.304 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.302e-03, size: 416, ETA: 2:15:55
2025-08-01 05:36:44.801 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 1.301e-03, size: 384, ETA: 2:15:52
2025-08-01 05:36:48.119 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 1.300e-03, size: 576, ETA: 2:15:47
2025-08-01 05:36:51.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 1.299e-03, size: 576, ETA: 2:15:44
2025-08-01 05:36:53.245 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:36:59.876 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:37:00.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:37:01.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4984
2025-08-01 05:37:01.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4249
2025-08-01 05:37:01.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2851
2025-08-01 05:37:01.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4028
2025-08-01 05:37:01.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.403
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:37:01.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:37:01.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:37:01.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:37:01.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:37:02.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:37:02.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:37:03.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:37:04.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:37:04.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:37:05.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:37:06.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:37:06.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:37:07.658 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:37:07.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 05:37:07.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 05:37:07.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:37:07.667 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.83 ms, Average inference time: 8.20 ms

2025-08-01 05:37:07.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:37:07.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:37:07.876 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch249
2025-08-01 05:37:11.245 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 1.298e-03, size: 416, ETA: 2:15:38
2025-08-01 05:37:14.508 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.297e-03, size: 320, ETA: 2:15:34
2025-08-01 05:37:17.720 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.297e-03, size: 544, ETA: 2:15:30
2025-08-01 05:37:21.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.296e-03, size: 576, ETA: 2:15:26
2025-08-01 05:37:24.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 1.295e-03, size: 352, ETA: 2:15:22
2025-08-01 05:37:28.051 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.294e-03, size: 416, ETA: 2:15:18
2025-08-01 05:37:29.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:37:36.321 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:37:37.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:37:37.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3636
2025-08-01 05:37:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3117
2025-08-01 05:37:37.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1367
2025-08-01 05:37:37.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2707
2025-08-01 05:37:37.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:37:37.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:37:37.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.137
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.271
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:37:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:37:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:37:38.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:37:38.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:37:39.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:37:40.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:37:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:37:41.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:37:41.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:37:42.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:37:43.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:37:43.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 05:37:43.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 05:37:43.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:37:43.060 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.81 ms, Average inference time: 8.30 ms

2025-08-01 05:37:43.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:37:43.193 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:37:43.276 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch250
2025-08-01 05:37:46.363 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.293e-03, size: 320, ETA: 2:15:12
2025-08-01 05:37:49.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 1.292e-03, size: 512, ETA: 2:15:08
2025-08-01 05:37:52.954 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.291e-03, size: 256, ETA: 2:15:03
2025-08-01 05:37:56.167 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 2.8, cls_loss: 0.7, lr: 1.291e-03, size: 576, ETA: 2:14:59
2025-08-01 05:37:59.602 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.290e-03, size: 576, ETA: 2:14:55
2025-08-01 05:38:03.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 2.7, iou_loss: 1.3, l1_loss: 0.3, conf_loss: 0.6, cls_loss: 0.5, lr: 1.289e-03, size: 352, ETA: 2:14:52
2025-08-01 05:38:04.662 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:38:11.308 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:38:13.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:38:14.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4623
2025-08-01 05:38:14.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4320
2025-08-01 05:38:14.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2664
2025-08-01 05:38:14.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3869
2025-08-01 05:38:14.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:38:14.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:38:14.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:38:14.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:38:16.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:38:18.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:38:19.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:38:21.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:38:23.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:38:25.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:38:26.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:38:28.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:38:30.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:38:30.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 05:38:30.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 05:38:30.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:38:30.378 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.85 ms, Average inference time: 8.28 ms

2025-08-01 05:38:30.380 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:38:30.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:38:30.616 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch251
2025-08-01 05:38:33.880 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.1, cls_loss: 0.5, lr: 1.288e-03, size: 320, ETA: 2:14:46
2025-08-01 05:38:37.267 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 1.287e-03, size: 544, ETA: 2:14:42
2025-08-01 05:38:40.699 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 1.286e-03, size: 352, ETA: 2:14:38
2025-08-01 05:38:43.987 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 1.286e-03, size: 256, ETA: 2:14:34
2025-08-01 05:38:47.323 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 3.4, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 1.285e-03, size: 480, ETA: 2:14:30
2025-08-01 05:38:50.560 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 1.284e-03, size: 352, ETA: 2:14:26
2025-08-01 05:38:52.128 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:38:58.760 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:38:59.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:38:59.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4566
2025-08-01 05:38:59.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4374
2025-08-01 05:38:59.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2215
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3718
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.222
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.372
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:38:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:38:59.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:38:59.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:38:59.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:38:59.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:38:59.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:38:59.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:38:59.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:39:00.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:39:00.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:39:01.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:39:01.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:39:02.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:39:02.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:39:03.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:39:03.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:39:03.944 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:39:03.944 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:39:03.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 05:39:03.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:39:03.959 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.83 ms, Average inference time: 8.32 ms

2025-08-01 05:39:03.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:39:04.131 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:39:04.217 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch252
2025-08-01 05:39:07.512 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.283e-03, size: 448, ETA: 2:14:20
2025-08-01 05:39:10.893 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, 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: 1.282e-03, size: 512, ETA: 2:14:16
2025-08-01 05:39:14.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.281e-03, size: 544, ETA: 2:14:12
2025-08-01 05:39:17.673 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 1.280e-03, size: 416, ETA: 2:14:08
2025-08-01 05:39:21.060 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.280e-03, size: 288, ETA: 2:14:04
2025-08-01 05:39:24.602 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.003s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 1.279e-03, size: 416, ETA: 2:14:00
2025-08-01 05:39:26.191 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:39:32.849 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:39:33.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:39:33.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5004
2025-08-01 05:39:33.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4343
2025-08-01 05:39:33.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2614
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3987
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.261
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.399
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:39:33.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:39:33.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:39:33.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:39:33.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:39:33.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:39:33.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:39:34.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:39:34.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:39:35.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:39:36.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:39:36.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:39:37.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:39:37.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:39:38.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:39:38.552 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:39:38.552 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 05:39:38.552 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 05:39:38.552 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:39:38.559 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.76 ms, Average inference time: 8.20 ms

2025-08-01 05:39:38.560 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:39:38.645 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:39:38.724 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch253
2025-08-01 05:39:41.866 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.9, lr: 1.278e-03, size: 352, ETA: 2:13:54
2025-08-01 05:39:45.272 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 1.277e-03, size: 288, ETA: 2:13:51
2025-08-01 05:39:48.732 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.276e-03, size: 544, ETA: 2:13:47
2025-08-01 05:39:52.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.275e-03, size: 448, ETA: 2:13:43
2025-08-01 05:39:55.408 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.3, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 1.0, lr: 1.275e-03, size: 384, ETA: 2:13:39
2025-08-01 05:39:58.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.274e-03, size: 256, ETA: 2:13:35
2025-08-01 05:40:00.218 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:40:06.875 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:40:07.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:40:07.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4303
2025-08-01 05:40:08.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3704
2025-08-01 05:40:08.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1398
2025-08-01 05:40:08.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3135
2025-08-01 05:40:08.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:40:08.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.140
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:40:08.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:40:08.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:40:08.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:40:09.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:40:09.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:40:10.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:40:10.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:40:11.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:40:11.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:40:12.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:40:12.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:40:12.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:40:12.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 05:40:12.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:40:12.734 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.83 ms, Average inference time: 8.26 ms

2025-08-01 05:40:12.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:40:12.819 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:40:12.899 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch254
2025-08-01 05:40:16.354 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 1.273e-03, size: 288, ETA: 2:13:29
2025-08-01 05:40:19.585 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 1.272e-03, size: 384, ETA: 2:13:25
2025-08-01 05:40:22.949 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.271e-03, size: 256, ETA: 2:13:21
2025-08-01 05:40:26.171 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.7, lr: 1.270e-03, size: 448, ETA: 2:13:17
2025-08-01 05:40:29.393 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 32.4, iou_loss: 4.8, l1_loss: 1.9, conf_loss: 25.0, cls_loss: 0.7, lr: 1.269e-03, size: 288, ETA: 2:13:13
2025-08-01 05:40:33.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.269e-03, size: 448, ETA: 2:13:09
2025-08-01 05:40:34.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:40:41.249 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:40:43.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:40:44.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4096
2025-08-01 05:40:44.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4129
2025-08-01 05:40:45.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2569
2025-08-01 05:40:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3598
2025-08-01 05:40:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:40:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:40:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-01 05:40:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 05:40:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-08-01 05:40:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.360
2025-08-01 05:40:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:40:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:40:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:40:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:40:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:40:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:40:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:40:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:40:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:40:46.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:40:48.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:40:50.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:40:52.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:40:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:40:55.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:40:57.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:40:59.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:41:01.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:41:01.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:41:01.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 05:41:01.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:41:01.355 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.88 ms, Average inference time: 8.25 ms

2025-08-01 05:41:01.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:41:01.444 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:41:01.529 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch255
2025-08-01 05:41:04.736 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, 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: 1.267e-03, size: 416, ETA: 2:13:03
2025-08-01 05:41:08.131 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 1.267e-03, size: 576, ETA: 2:12:59
2025-08-01 05:41:11.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.266e-03, size: 288, ETA: 2:12:55
2025-08-01 05:41:14.932 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.265e-03, size: 384, ETA: 2:12:52
2025-08-01 05:41:18.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.264e-03, size: 416, ETA: 2:12:47
2025-08-01 05:41:21.488 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.263e-03, size: 480, ETA: 2:12:43
2025-08-01 05:41:22.947 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:41:29.634 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:41:30.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:41:30.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4479
2025-08-01 05:41:30.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3490
2025-08-01 05:41:30.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2494
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3488
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.349
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:41:30.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:41:30.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:41:30.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:41:30.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:41:30.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:41:30.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:41:30.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:41:30.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:41:31.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:41:31.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:41:32.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:41:32.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:41:33.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:41:33.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:41:33.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:41:34.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:41:34.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:41:34.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:41:34.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 05:41:34.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:41:34.852 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.81 ms, Average inference time: 8.15 ms

2025-08-01 05:41:34.853 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:41:34.934 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:41:35.014 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch256
2025-08-01 05:41:38.335 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.262e-03, size: 512, ETA: 2:12:38
2025-08-01 05:41:41.717 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.7, lr: 1.262e-03, size: 480, ETA: 2:12:34
2025-08-01 05:41:44.998 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.261e-03, size: 544, ETA: 2:12:29
2025-08-01 05:41:48.284 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, 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: 1.260e-03, size: 544, ETA: 2:12:25
2025-08-01 05:41:51.554 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.9, lr: 1.259e-03, size: 384, ETA: 2:12:21
2025-08-01 05:41:55.112 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.8, lr: 1.258e-03, size: 288, ETA: 2:12:17
2025-08-01 05:41:56.670 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:42:03.424 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:42:04.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:42:04.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5009
2025-08-01 05:42:04.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4331
2025-08-01 05:42:04.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3238
2025-08-01 05:42:04.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4193
2025-08-01 05:42:04.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:42:04.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:42:04.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-01 05:42:04.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.419
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:42:04.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:42:04.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:42:05.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:42:06.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:42:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:42:07.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:42:08.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:42:08.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:42:09.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:42:10.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:42:10.711 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:42:10.711 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 05:42:10.711 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 05:42:10.711 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:42:10.722 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.81 ms, Average inference time: 8.31 ms

2025-08-01 05:42:10.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:42:10.843 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:42:10.924 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch257
2025-08-01 05:42:14.365 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.9, lr: 1.257e-03, size: 544, ETA: 2:12:12
2025-08-01 05:42:17.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.256e-03, size: 352, ETA: 2:12:08
2025-08-01 05:42:21.432 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.256e-03, size: 256, ETA: 2:12:05
2025-08-01 05:42:24.591 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.255e-03, size: 320, ETA: 2:12:00
2025-08-01 05:42:27.981 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 1.1, lr: 1.254e-03, size: 512, ETA: 2:11:56
2025-08-01 05:42:31.423 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.9, lr: 1.253e-03, size: 576, ETA: 2:11:52
2025-08-01 05:42:33.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:42:39.784 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:42:40.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:42:41.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3637
2025-08-01 05:42:41.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2547
2025-08-01 05:42:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2423
2025-08-01 05:42:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2869
2025-08-01 05:42:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:42:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.255
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.287
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:42:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:42:41.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:42:41.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:42:41.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:42:41.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:42:42.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:42:43.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:42:43.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:42:44.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:42:44.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:42:45.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:42:45.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:42:46.397 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:42:46.397 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 05:42:46.397 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 05:42:46.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:42:46.407 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.84 ms, Average inference time: 8.32 ms

2025-08-01 05:42:46.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:42:46.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:42:46.579 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch258
2025-08-01 05:42:49.731 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.8, lr: 1.252e-03, size: 544, ETA: 2:11:47
2025-08-01 05:42:53.154 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.251e-03, size: 448, ETA: 2:11:43
2025-08-01 05:42:56.494 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, 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: 1.250e-03, size: 416, ETA: 2:11:39
2025-08-01 05:43:00.031 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 4.0, cls_loss: 0.9, lr: 1.250e-03, size: 256, ETA: 2:11:35
2025-08-01 05:43:03.476 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 1.249e-03, size: 320, ETA: 2:11:31
2025-08-01 05:43:06.925 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 1.248e-03, size: 256, ETA: 2:11:27
2025-08-01 05:43:08.282 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:43:15.106 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:43:17.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:43:18.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3866
2025-08-01 05:43:18.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2655
2025-08-01 05:43:18.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1995
2025-08-01 05:43:18.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2839
2025-08-01 05:43:18.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:43:18.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:43:18.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 05:43:18.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-08-01 05:43:18.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.200
2025-08-01 05:43:18.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.284
2025-08-01 05:43:18.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:43:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:43:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:43:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:43:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:43:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:43:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:43:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:43:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:43:20.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:43:21.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:43:23.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:43:24.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:43:26.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:43:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:43:29.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:43:30.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:43:32.546 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:43:32.546 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:43:32.546 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 05:43:32.546 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:43:32.571 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.85 ms, Average inference time: 8.32 ms

2025-08-01 05:43:32.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:43:32.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:43:32.789 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch259
2025-08-01 05:43:35.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.247e-03, size: 256, ETA: 2:11:21
2025-08-01 05:43:39.344 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, 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: 1.246e-03, size: 416, ETA: 2:11:17
2025-08-01 05:43:42.557 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, 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: 1.245e-03, size: 480, ETA: 2:11:13
2025-08-01 05:43:45.887 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.8, lr: 1.244e-03, size: 448, ETA: 2:11:09
2025-08-01 05:43:49.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.2, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 0.9, lr: 1.244e-03, size: 416, ETA: 2:11:05
2025-08-01 05:43:52.689 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.243e-03, size: 512, ETA: 2:11:01
2025-08-01 05:43:54.259 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:44:00.946 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:44:01.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:44:01.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4245
2025-08-01 05:44:01.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3899
2025-08-01 05:44:01.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2031
2025-08-01 05:44:01.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3392
2025-08-01 05:44:01.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:44:01.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:44:01.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-01 05:44:01.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-01 05:44:01.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.203
2025-08-01 05:44:01.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.339
2025-08-01 05:44:01.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:44:01.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:44:01.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:44:01.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:44:01.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:44:01.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:44:01.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:44:01.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:44:01.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:44:02.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:44:02.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:44:02.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:44:03.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:44:03.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:44:03.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:44:04.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:44:04.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:44:04.813 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:44:04.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:44:04.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:44:04.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:44:04.820 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.78 ms, Average inference time: 8.29 ms

2025-08-01 05:44:04.821 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:44:04.941 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:44:05.082 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch260
2025-08-01 05:44:08.539 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 0.8, lr: 1.242e-03, size: 544, ETA: 2:10:56
2025-08-01 05:44:11.878 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.241e-03, size: 320, ETA: 2:10:52
2025-08-01 05:44:15.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.240e-03, size: 576, ETA: 2:10:48
2025-08-01 05:44:18.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.8, lr: 1.239e-03, size: 448, ETA: 2:10:44
2025-08-01 05:44:22.085 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 1.238e-03, size: 256, ETA: 2:10:40
2025-08-01 05:44:25.742 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 1.238e-03, size: 512, ETA: 2:10:37
2025-08-01 05:44:27.307 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:44:33.836 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:44:34.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:44:35.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4751
2025-08-01 05:44:35.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4122
2025-08-01 05:44:35.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2391
2025-08-01 05:44:35.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3754
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.239
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.375
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:44:35.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:44:35.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:44:35.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:44:35.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:44:35.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:44:35.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:44:36.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:44:37.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:44:37.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:44:38.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:44:39.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:44:39.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:44:40.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:44:40.996 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:44:40.996 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:44:40.996 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 05:44:40.996 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:44:41.003 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.82 ms, Average inference time: 8.32 ms

2025-08-01 05:44:41.005 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:44:41.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:44:41.166 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch261
2025-08-01 05:44:44.469 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 1.237e-03, size: 288, ETA: 2:10:31
2025-08-01 05:44:47.843 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.9, lr: 1.236e-03, size: 352, ETA: 2:10:27
2025-08-01 05:44:51.259 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.7, lr: 1.235e-03, size: 256, ETA: 2:10:23
2025-08-01 05:44:54.855 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 0.7, lr: 1.234e-03, size: 448, ETA: 2:10:19
2025-08-01 05:44:58.395 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.6, lr: 1.233e-03, size: 576, ETA: 2:10:16
2025-08-01 05:45:01.589 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 2.7, cls_loss: 0.7, lr: 1.233e-03, size: 448, ETA: 2:10:12
2025-08-01 05:45:03.048 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:45:09.781 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:45:10.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:45:10.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4811
2025-08-01 05:45:11.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3823
2025-08-01 05:45:11.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2754
2025-08-01 05:45:11.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3796
2025-08-01 05:45:11.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:45:11.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:45:11.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 05:45:11.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-01 05:45:11.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-08-01 05:45:11.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:45:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:45:11.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:45:12.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:45:12.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:45:13.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:45:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:45:14.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:45:15.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:45:15.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:45:16.524 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:45:16.524 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:45:16.524 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 05:45:16.524 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:45:16.531 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.81 ms, Average inference time: 8.35 ms

2025-08-01 05:45:16.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:45:16.622 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:45:16.754 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch262
2025-08-01 05:45:20.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.231e-03, size: 448, ETA: 2:10:06
2025-08-01 05:45:23.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.231e-03, size: 512, ETA: 2:10:02
2025-08-01 05:45:26.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.5, lr: 1.230e-03, size: 544, ETA: 2:09:58
2025-08-01 05:45:30.241 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 1.229e-03, size: 576, ETA: 2:09:54
2025-08-01 05:45:33.672 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.228e-03, size: 384, ETA: 2:09:50
2025-08-01 05:45:36.916 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 1.227e-03, size: 416, ETA: 2:09:46
2025-08-01 05:45:38.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:45:45.269 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:45:46.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:45:46.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4378
2025-08-01 05:45:46.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3931
2025-08-01 05:45:46.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1913
2025-08-01 05:45:46.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3407
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.341
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:45:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:45:46.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:45:46.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:45:46.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:45:46.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:45:47.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:45:48.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:45:49.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:45:49.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:45:50.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:45:51.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:45:52.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:45:52.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:45:53.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:45:53.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:45:53.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:45:53.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:45:53.547 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.85 ms, Average inference time: 8.36 ms

2025-08-01 05:45:53.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:45:53.686 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:45:53.767 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch263
2025-08-01 05:45:57.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.226e-03, size: 544, ETA: 2:09:40
2025-08-01 05:46:00.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.225e-03, size: 576, ETA: 2:09:37
2025-08-01 05:46:03.807 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.225e-03, size: 512, ETA: 2:09:33
2025-08-01 05:46:07.204 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.224e-03, size: 416, ETA: 2:09:29
2025-08-01 05:46:10.659 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 1.8, cls_loss: 0.7, lr: 1.223e-03, size: 544, ETA: 2:09:25
2025-08-01 05:46:14.068 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 1.222e-03, size: 384, ETA: 2:09:21
2025-08-01 05:46:15.682 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:46:22.285 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:46:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:46:22.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3592
2025-08-01 05:46:23.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2854
2025-08-01 05:46:23.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1408
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2618
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.141
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.262
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:46:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:46:23.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:46:23.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:46:23.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:46:23.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:46:23.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:46:23.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:46:23.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:46:23.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:46:23.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:46:24.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:46:24.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:46:24.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:46:25.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:46:25.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:46:25.898 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:46:25.898 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:46:25.898 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 05:46:25.898 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:46:25.904 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.74 ms, Average inference time: 8.25 ms

2025-08-01 05:46:25.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:46:25.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:46:26.057 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch264
2025-08-01 05:46:29.382 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.221e-03, size: 480, ETA: 2:09:15
2025-08-01 05:46:32.669 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.220e-03, size: 352, ETA: 2:09:11
2025-08-01 05:46:36.193 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 1.219e-03, size: 352, ETA: 2:09:08
2025-08-01 05:46:39.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.219e-03, size: 544, ETA: 2:09:04
2025-08-01 05:46:42.953 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 1.218e-03, size: 288, ETA: 2:09:00
2025-08-01 05:46:46.407 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.217e-03, size: 512, ETA: 2:08:56
2025-08-01 05:46:47.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:46:54.530 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:46:55.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:46:55.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4075
2025-08-01 05:46:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3465
2025-08-01 05:46:55.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2028
2025-08-01 05:46:55.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3189
2025-08-01 05:46:55.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:46:55.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:46:55.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-01 05:46:55.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.346
2025-08-01 05:46:55.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.203
2025-08-01 05:46:55.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.319
2025-08-01 05:46:55.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:46:55.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:46:55.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:46:55.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:46:55.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:46:55.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:46:55.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:46:55.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:46:55.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:46:56.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:46:56.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:46:57.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:46:57.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:46:58.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:46:58.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:46:59.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:47:00.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:47:00.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:47:00.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:47:00.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 05:47:00.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:47:00.532 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.81 ms, Average inference time: 8.29 ms

2025-08-01 05:47:00.533 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:47:00.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:47:00.693 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch265
2025-08-01 05:47:03.768 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 1.216e-03, size: 384, ETA: 2:08:50
2025-08-01 05:47:07.144 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.215e-03, size: 512, ETA: 2:08:46
2025-08-01 05:47:10.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.8, iou_loss: 2.7, l1_loss: 1.6, conf_loss: 3.7, cls_loss: 0.8, lr: 1.214e-03, size: 544, ETA: 2:08:42
2025-08-01 05:47:13.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 1.213e-03, size: 320, ETA: 2:08:38
2025-08-01 05:47:17.191 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.213e-03, size: 480, ETA: 2:08:34
2025-08-01 05:47:20.437 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.212e-03, size: 384, ETA: 2:08:30
2025-08-01 05:47:21.938 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:47:28.809 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:47:29.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:47:30.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4960
2025-08-01 05:47:30.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3801
2025-08-01 05:47:30.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2903
2025-08-01 05:47:30.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3888
2025-08-01 05:47:30.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:47:30.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:47:30.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-01 05:47:30.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-01 05:47:30.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-01 05:47:30.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.389
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:47:30.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:47:31.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:47:32.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:47:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:47:33.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:47:34.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:47:35.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:47:35.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:47:36.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:47:37.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:47:37.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:47:37.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 05:47:37.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:47:37.203 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.86 ms, Average inference time: 8.30 ms

2025-08-01 05:47:37.206 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:47:37.290 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:47:37.397 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch266
2025-08-01 05:47:40.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 1.1, lr: 1.211e-03, size: 480, ETA: 2:08:24
2025-08-01 05:47:43.972 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.8, lr: 1.210e-03, size: 544, ETA: 2:08:20
2025-08-01 05:47:47.279 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.209e-03, size: 512, ETA: 2:08:16
2025-08-01 05:47:50.832 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.9, lr: 1.208e-03, size: 416, ETA: 2:08:13
2025-08-01 05:47:54.079 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.005s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.2, cls_loss: 0.8, lr: 1.207e-03, size: 416, ETA: 2:08:09
2025-08-01 05:47:57.409 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 1.0, lr: 1.207e-03, size: 416, ETA: 2:08:05
2025-08-01 05:47:59.044 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:48:05.762 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:48:06.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:48:07.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5256
2025-08-01 05:48:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4541
2025-08-01 05:48:07.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2471
2025-08-01 05:48:07.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4089
2025-08-01 05:48:07.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:48:07.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.247
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:48:07.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:48:07.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:48:07.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:48:08.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:48:08.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:48:09.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:48:10.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:48:11.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:48:11.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:48:12.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:48:13.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:48:14.167 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:48:14.167 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 05:48:14.167 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 05:48:14.167 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:48:14.174 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.83 ms, Average inference time: 8.27 ms

2025-08-01 05:48:14.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:48:14.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:48:14.376 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch267
2025-08-01 05:48:17.620 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 1.205e-03, size: 288, ETA: 2:07:59
2025-08-01 05:48:20.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 1.205e-03, size: 288, ETA: 2:07:55
2025-08-01 05:48:24.347 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 1.204e-03, size: 576, ETA: 2:07:51
2025-08-01 05:48:27.895 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.203e-03, size: 352, ETA: 2:07:48
2025-08-01 05:48:31.318 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 1.202e-03, size: 288, ETA: 2:07:44
2025-08-01 05:48:34.571 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 1.201e-03, size: 288, ETA: 2:07:40
2025-08-01 05:48:36.074 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:48:42.844 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:48:43.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:48:43.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4997
2025-08-01 05:48:44.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3865
2025-08-01 05:48:44.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3096
2025-08-01 05:48:44.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3986
2025-08-01 05:48:44.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:48:44.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:48:44.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-01 05:48:44.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 05:48:44.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.399
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:48:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:48:44.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:48:45.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:48:45.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:48:46.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:48:46.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:48:47.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:48:48.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:48:48.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:48:49.094 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:48:49.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:48:49.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 05:48:49.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:48:49.102 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.84 ms, Average inference time: 8.34 ms

2025-08-01 05:48:49.103 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:48:49.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:48:49.343 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch268
2025-08-01 05:48:52.613 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.200e-03, size: 352, ETA: 2:07:34
2025-08-01 05:48:56.117 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.199e-03, size: 256, ETA: 2:07:30
2025-08-01 05:48:59.532 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.8, lr: 1.199e-03, size: 576, ETA: 2:07:27
2025-08-01 05:49:03.251 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.198e-03, size: 512, ETA: 2:07:23
2025-08-01 05:49:06.914 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 1.197e-03, size: 544, ETA: 2:07:19
2025-08-01 05:49:10.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.196e-03, size: 512, ETA: 2:07:16
2025-08-01 05:49:12.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:49:18.752 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:49:19.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:49:19.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4736
2025-08-01 05:49:19.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3949
2025-08-01 05:49:19.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2720
2025-08-01 05:49:19.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3802
2025-08-01 05:49:19.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:49:19.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:49:19.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-01 05:49:19.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-01 05:49:19.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-08-01 05:49:19.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:49:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:49:20.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:49:20.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:49:20.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:49:21.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:49:21.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:49:21.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:49:22.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:49:22.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:49:23.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:49:23.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:49:23.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 05:49:23.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:49:23.092 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.72 ms, Average inference time: 8.20 ms

2025-08-01 05:49:23.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:49:23.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:49:23.281 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch269
2025-08-01 05:49:26.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.5, lr: 1.195e-03, size: 320, ETA: 2:07:10
2025-08-01 05:49:29.785 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.8, lr: 1.194e-03, size: 256, ETA: 2:07:06
2025-08-01 05:49:33.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 1.193e-03, size: 384, ETA: 2:07:02
2025-08-01 05:49:36.563 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.8, lr: 1.193e-03, size: 576, ETA: 2:06:58
2025-08-01 05:49:40.078 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, 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: 1.192e-03, size: 544, ETA: 2:06:55
2025-08-01 05:49:43.563 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.191e-03, size: 384, ETA: 2:06:51
2025-08-01 05:49:45.096 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:49:51.798 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:49:52.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:49:53.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4778
2025-08-01 05:49:53.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3917
2025-08-01 05:49:53.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2545
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3746
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.375
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:49:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:49:53.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:49:53.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:49:53.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:49:53.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:49:53.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:49:53.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:49:53.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:49:54.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:49:54.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:49:55.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:49:56.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:49:56.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:49:57.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:49:58.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:49:58.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:49:59.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:49:59.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:49:59.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 05:49:59.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:49:59.549 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.84 ms, Average inference time: 8.24 ms

2025-08-01 05:49:59.550 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:49:59.627 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:49:59.759 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch270
2025-08-01 05:50:02.936 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 1.190e-03, size: 416, ETA: 2:06:45
2025-08-01 05:50:06.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.189e-03, size: 320, ETA: 2:06:41
2025-08-01 05:50:09.653 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 1.2, lr: 1.188e-03, size: 512, ETA: 2:06:37
2025-08-01 05:50:13.070 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.187e-03, size: 480, ETA: 2:06:34
2025-08-01 05:50:16.591 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.6, lr: 1.186e-03, size: 448, ETA: 2:06:30
2025-08-01 05:50:19.973 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.186e-03, size: 416, ETA: 2:06:26
2025-08-01 05:50:21.481 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:50:28.310 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:50:29.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:50:29.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4912
2025-08-01 05:50:29.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4289
2025-08-01 05:50:30.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2514
2025-08-01 05:50:30.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3905
2025-08-01 05:50:30.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:50:30.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:50:30.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.251
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.391
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:50:30.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:50:30.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:50:30.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:50:30.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:50:30.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:50:31.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:50:32.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:50:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:50:33.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:50:34.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:50:35.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:50:36.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:50:36.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:50:36.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:50:36.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 05:50:36.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:50:36.966 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.87 ms, Average inference time: 8.27 ms

2025-08-01 05:50:36.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:50:37.052 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:50:37.132 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch271
2025-08-01 05:50:40.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 3.3, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 1.1, cls_loss: 0.5, lr: 1.184e-03, size: 384, ETA: 2:06:20
2025-08-01 05:50:43.780 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 1.184e-03, size: 448, ETA: 2:06:16
2025-08-01 05:50:47.120 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.183e-03, size: 384, ETA: 2:06:12
2025-08-01 05:50:50.446 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 1.0, lr: 1.182e-03, size: 448, ETA: 2:06:08
2025-08-01 05:50:53.962 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.181e-03, size: 480, ETA: 2:06:05
2025-08-01 05:50:57.602 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.7, lr: 1.180e-03, size: 576, ETA: 2:06:01
2025-08-01 05:50:59.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:51:06.066 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:51:07.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:51:07.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4552
2025-08-01 05:51:08.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3847
2025-08-01 05:51:08.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1815
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3404
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.340
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:51:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:51:08.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:51:08.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:51:08.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:51:08.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:51:08.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:51:09.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:51:09.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:51:10.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:51:11.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:51:12.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:51:13.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:51:14.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:51:15.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:51:16.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:51:16.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:51:16.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:51:16.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:51:16.373 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.86 ms, Average inference time: 8.31 ms

2025-08-01 05:51:16.374 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:51:16.451 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:51:16.531 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch272
2025-08-01 05:51:19.717 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.179e-03, size: 416, ETA: 2:05:56
2025-08-01 05:51:22.986 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 1.178e-03, size: 384, ETA: 2:05:52
2025-08-01 05:51:26.261 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.178e-03, size: 320, ETA: 2:05:48
2025-08-01 05:51:29.540 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 0.8, lr: 1.177e-03, size: 512, ETA: 2:05:44
2025-08-01 05:51:33.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.6, lr: 1.176e-03, size: 576, ETA: 2:05:40
2025-08-01 05:51:36.407 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.175e-03, size: 256, ETA: 2:05:36
2025-08-01 05:51:37.744 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:51:44.301 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:51:45.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:51:45.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3703
2025-08-01 05:51:45.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3023
2025-08-01 05:51:45.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1895
2025-08-01 05:51:45.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2874
2025-08-01 05:51:45.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:51:45.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:51:45.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.190
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.287
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:51:45.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:51:45.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:51:46.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:51:47.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:51:48.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:51:48.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:51:49.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:51:49.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:51:50.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:51:51.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:51:52.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:51:52.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:51:52.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 05:51:52.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:51:52.045 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.83 ms, Average inference time: 8.21 ms

2025-08-01 05:51:52.048 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:51:52.128 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:51:52.210 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch273
2025-08-01 05:51:55.585 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 1.174e-03, size: 416, ETA: 2:05:30
2025-08-01 05:51:58.928 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.173e-03, size: 544, ETA: 2:05:26
2025-08-01 05:52:02.251 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.8, lr: 1.172e-03, size: 352, ETA: 2:05:22
2025-08-01 05:52:05.816 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.9, lr: 1.172e-03, size: 448, ETA: 2:05:19
2025-08-01 05:52:09.143 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 1.171e-03, size: 480, ETA: 2:05:15
2025-08-01 05:52:12.344 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.9, lr: 1.170e-03, size: 320, ETA: 2:05:11
2025-08-01 05:52:13.870 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:52:20.436 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:52:21.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:52:21.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2264
2025-08-01 05:52:21.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2171
2025-08-01 05:52:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0655
2025-08-01 05:52:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1697
2025-08-01 05:52:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:52:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:52:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.226
2025-08-01 05:52:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-08-01 05:52:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.066
2025-08-01 05:52:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.170
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:52:22.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:52:22.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:52:23.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:52:23.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:52:24.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:52:25.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:52:25.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:52:26.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:52:26.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:52:27.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:52:27.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 05:52:27.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.17
2025-08-01 05:52:27.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:52:27.548 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.87 ms, Average inference time: 8.35 ms

2025-08-01 05:52:27.548 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:52:27.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:52:27.716 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch274
2025-08-01 05:52:30.978 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.169e-03, size: 576, ETA: 2:05:05
2025-08-01 05:52:34.428 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 3.6, cls_loss: 0.6, lr: 1.168e-03, size: 384, ETA: 2:05:01
2025-08-01 05:52:37.752 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.167e-03, size: 320, ETA: 2:04:57
2025-08-01 05:52:41.327 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 1.166e-03, size: 384, ETA: 2:04:54
2025-08-01 05:52:44.779 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.165e-03, size: 288, ETA: 2:04:50
2025-08-01 05:52:48.190 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.165e-03, size: 512, ETA: 2:04:46
2025-08-01 05:52:49.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:52:56.280 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:52:57.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:52:57.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5237
2025-08-01 05:52:57.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4625
2025-08-01 05:52:57.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3407
2025-08-01 05:52:57.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4423
2025-08-01 05:52:57.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:52:57.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:52:57.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-08-01 05:52:57.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-01 05:52:57.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-08-01 05:52:57.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-08-01 05:52:57.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:52:57.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:52:57.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:52:57.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:52:57.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:52:57.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:52:57.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:52:57.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:52:57.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:52:58.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:52:59.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:52:59.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:53:00.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:53:01.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:53:01.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:53:02.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:53:03.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:53:03.817 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:53:03.817 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 05:53:03.817 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-01 05:53:03.817 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:53:03.825 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.82 ms, Average inference time: 8.23 ms

2025-08-01 05:53:03.826 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:53:03.949 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:53:04.043 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch275
2025-08-01 05:53:07.347 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.7, lr: 1.164e-03, size: 480, ETA: 2:04:41
2025-08-01 05:53:10.752 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.6, lr: 1.163e-03, size: 480, ETA: 2:04:37
2025-08-01 05:53:14.119 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.8, lr: 1.162e-03, size: 256, ETA: 2:04:33
2025-08-01 05:53:17.302 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 2.3, cls_loss: 0.7, lr: 1.161e-03, size: 384, ETA: 2:04:29
2025-08-01 05:53:20.841 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.6, lr: 1.160e-03, size: 576, ETA: 2:04:25
2025-08-01 05:53:24.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 1.2, lr: 1.159e-03, size: 256, ETA: 2:04:21
2025-08-01 05:53:25.797 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:53:32.507 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:53:32.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:53:33.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4584
2025-08-01 05:53:33.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3506
2025-08-01 05:53:33.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2832
2025-08-01 05:53:33.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3641
2025-08-01 05:53:33.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:53:33.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:53:33.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-01 05:53:33.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-08-01 05:53:33.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.283
2025-08-01 05:53:33.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.364
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:53:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:53:33.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:53:34.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:53:34.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:53:34.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:53:35.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:53:35.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:53:35.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:53:36.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:53:36.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:53:36.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:53:36.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 05:53:36.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:53:36.471 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.79 ms, Average inference time: 8.34 ms

2025-08-01 05:53:36.472 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:53:36.555 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:53:36.635 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch276
2025-08-01 05:53:39.720 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 1.158e-03, size: 320, ETA: 2:04:15
2025-08-01 05:53:43.135 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.7, lr: 1.157e-03, size: 512, ETA: 2:04:12
2025-08-01 05:53:46.524 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.157e-03, size: 416, ETA: 2:04:08
2025-08-01 05:53:50.131 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, 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: 1.156e-03, size: 320, ETA: 2:04:04
2025-08-01 05:53:53.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.155e-03, size: 384, ETA: 2:04:00
2025-08-01 05:53:56.877 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.154e-03, size: 544, ETA: 2:03:57
2025-08-01 05:53:58.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:54:05.218 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:54:06.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:54:07.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4446
2025-08-01 05:54:07.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3807
2025-08-01 05:54:07.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1863
2025-08-01 05:54:07.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3372
2025-08-01 05:54:07.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:54:07.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:54:07.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-01 05:54:07.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-01 05:54:07.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-08-01 05:54:07.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-08-01 05:54:07.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:54:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:54:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:54:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:54:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:54:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:54:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:54:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:54:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:54:08.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:54:09.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:54:10.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:54:12.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:54:13.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:54:14.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:54:15.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:54:16.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:54:17.536 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:54:17.536 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:54:17.536 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:54:17.536 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:54:17.551 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.86 ms, Average inference time: 8.29 ms

2025-08-01 05:54:17.552 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:54:17.665 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:54:17.781 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch277
2025-08-01 05:54:20.874 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.153e-03, size: 320, ETA: 2:03:51
2025-08-01 05:54:24.250 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.152e-03, size: 544, ETA: 2:03:47
2025-08-01 05:54:27.541 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.8, lr: 1.151e-03, size: 320, ETA: 2:03:43
2025-08-01 05:54:30.838 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.5, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 0.8, lr: 1.151e-03, size: 576, ETA: 2:03:39
2025-08-01 05:54:34.481 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.5, lr: 1.150e-03, size: 448, ETA: 2:03:35
2025-08-01 05:54:37.771 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.5, lr: 1.149e-03, size: 416, ETA: 2:03:31
2025-08-01 05:54:39.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:54:45.770 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:54:46.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:54:46.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3847
2025-08-01 05:54:46.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3515
2025-08-01 05:54:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1516
2025-08-01 05:54:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2959
2025-08-01 05:54:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:54:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:54:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-01 05:54:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-08-01 05:54:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.152
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.296
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:54:46.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:54:46.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:54:47.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:54:47.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:54:47.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:54:48.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:54:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:54:48.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:54:49.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:54:49.458 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:54:49.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 05:54:49.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 05:54:49.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:54:49.465 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.74 ms, Average inference time: 8.20 ms

2025-08-01 05:54:49.466 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:54:49.549 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:54:49.629 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch278
2025-08-01 05:54:53.000 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, 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: 1.148e-03, size: 320, ETA: 2:03:26
2025-08-01 05:54:56.282 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.147e-03, size: 416, ETA: 2:03:22
2025-08-01 05:54:59.630 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.8, lr: 1.146e-03, size: 288, ETA: 2:03:18
2025-08-01 05:55:02.915 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.145e-03, size: 544, ETA: 2:03:14
2025-08-01 05:55:06.560 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.144e-03, size: 384, ETA: 2:03:10
2025-08-01 05:55:09.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 4.2, cls_loss: 0.9, lr: 1.144e-03, size: 576, ETA: 2:03:06
2025-08-01 05:55:11.519 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:55:18.217 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:55:18.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:55:19.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4312
2025-08-01 05:55:19.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3687
2025-08-01 05:55:19.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2094
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3364
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:55:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:55:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:55:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:55:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:55:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:55:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:55:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:55:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:55:19.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:55:20.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:55:20.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:55:21.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:55:21.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:55:22.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:55:22.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:55:23.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:55:23.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:55:23.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:55:23.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 05:55:23.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:55:23.610 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.79 ms, Average inference time: 8.17 ms

2025-08-01 05:55:23.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:55:23.698 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:55:23.817 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch279
2025-08-01 05:55:27.268 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.142e-03, size: 416, ETA: 2:03:01
2025-08-01 05:55:30.423 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.142e-03, size: 352, ETA: 2:02:57
2025-08-01 05:55:33.640 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.141e-03, size: 352, ETA: 2:02:53
2025-08-01 05:55:37.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.140e-03, size: 480, ETA: 2:02:49
2025-08-01 05:55:40.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.8, lr: 1.139e-03, size: 256, ETA: 2:02:46
2025-08-01 05:55:43.719 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.138e-03, size: 352, ETA: 2:02:41
2025-08-01 05:55:45.286 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:55:51.951 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:55:52.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:55:53.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4811
2025-08-01 05:55:53.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4074
2025-08-01 05:55:53.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2797
2025-08-01 05:55:53.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3894
2025-08-01 05:55:53.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:55:53.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:55:53.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 05:55:53.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 05:55:53.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-08-01 05:55:53.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.389
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:55:53.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:55:54.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:55:55.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:55:56.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:55:56.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:55:57.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:55:58.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:55:59.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:56:00.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:56:00.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:56:00.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 05:56:00.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 05:56:00.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:56:00.977 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.83 ms, Average inference time: 8.25 ms

2025-08-01 05:56:00.978 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:56:01.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:56:01.169 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch280
2025-08-01 05:56:04.656 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.6, cls_loss: 0.8, lr: 1.137e-03, size: 256, ETA: 2:02:36
2025-08-01 05:56:07.886 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.8, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.5, lr: 1.136e-03, size: 512, ETA: 2:02:32
2025-08-01 05:56:11.250 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 1.136e-03, size: 544, ETA: 2:02:28
2025-08-01 05:56:14.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 1.135e-03, size: 416, ETA: 2:02:25
2025-08-01 05:56:17.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.9, lr: 1.134e-03, size: 288, ETA: 2:02:21
2025-08-01 05:56:21.421 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.9, lr: 1.133e-03, size: 384, ETA: 2:02:17
2025-08-01 05:56:22.946 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:56:29.522 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:56:30.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:56:30.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3601
2025-08-01 05:56:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2667
2025-08-01 05:56:30.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1684
2025-08-01 05:56:30.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2650
2025-08-01 05:56:30.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.168
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.265
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:56:30.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:56:30.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:56:30.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:56:31.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:56:31.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:56:32.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:56:32.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:56:32.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:56:33.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:56:33.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:56:34.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:56:34.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 05:56:34.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 05:56:34.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:56:34.187 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.79 ms, Average inference time: 8.28 ms

2025-08-01 05:56:34.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:56:34.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:56:34.347 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch281
2025-08-01 05:56:37.508 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 10.2, iou_loss: 3.3, l1_loss: 1.4, conf_loss: 4.6, cls_loss: 1.0, lr: 1.132e-03, size: 352, ETA: 2:02:11
2025-08-01 05:56:40.825 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.131e-03, size: 448, ETA: 2:02:07
2025-08-01 05:56:44.247 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 1.130e-03, size: 512, ETA: 2:02:03
2025-08-01 05:56:47.910 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.8, lr: 1.129e-03, size: 384, ETA: 2:02:00
2025-08-01 05:56:51.460 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, 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: 1.129e-03, size: 480, ETA: 2:01:56
2025-08-01 05:56:55.089 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.9, lr: 1.128e-03, size: 576, ETA: 2:01:53
2025-08-01 05:56:56.800 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:57:03.605 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:57:04.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:57:04.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3998
2025-08-01 05:57:04.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3562
2025-08-01 05:57:04.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2288
2025-08-01 05:57:04.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3283
2025-08-01 05:57:04.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:57:04.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:57:04.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 05:57:04.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.229
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.328
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:57:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:57:04.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:57:04.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:57:05.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:57:05.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:57:05.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:57:06.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:57:06.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:57:07.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:57:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:57:07.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:57:08.284 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:57:08.284 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 05:57:08.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 05:57:08.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:57:08.291 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.78 ms, Average inference time: 8.38 ms

2025-08-01 05:57:08.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:57:08.374 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:57:08.452 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch282
2025-08-01 05:57:11.887 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 1.127e-03, size: 544, ETA: 2:01:47
2025-08-01 05:57:15.539 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.5, l1_loss: 1.9, conf_loss: 2.7, cls_loss: 0.9, lr: 1.126e-03, size: 544, ETA: 2:01:44
2025-08-01 05:57:18.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.9, lr: 1.125e-03, size: 320, ETA: 2:01:40
2025-08-01 05:57:22.024 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.5, lr: 1.124e-03, size: 352, ETA: 2:01:36
2025-08-01 05:57:25.261 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.8, lr: 1.123e-03, size: 288, ETA: 2:01:32
2025-08-01 05:57:28.520 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 1.123e-03, size: 288, ETA: 2:01:28
2025-08-01 05:57:29.878 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:57:36.549 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:57:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:57:38.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4647
2025-08-01 05:57:38.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3723
2025-08-01 05:57:38.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2077
2025-08-01 05:57:38.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3482
2025-08-01 05:57:38.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:57:38.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:57:38.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-01 05:57:38.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-01 05:57:38.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.208
2025-08-01 05:57:38.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.348
2025-08-01 05:57:38.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:57:38.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:57:38.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:57:38.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:57:38.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:57:38.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:57:38.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:57:38.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:57:38.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:57:39.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:57:40.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:57:41.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:57:42.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:57:43.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:57:43.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:57:44.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:57:45.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:57:46.478 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:57:46.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 05:57:46.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 05:57:46.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:57:46.490 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.84 ms, Average inference time: 8.34 ms

2025-08-01 05:57:46.491 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:57:46.602 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:57:46.722 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch283
2025-08-01 05:57:50.026 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.7, conf_loss: 2.4, cls_loss: 0.7, lr: 1.121e-03, size: 512, ETA: 2:01:22
2025-08-01 05:57:53.303 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 1.121e-03, size: 288, ETA: 2:01:18
2025-08-01 05:57:56.841 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 9.5, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 4.4, cls_loss: 0.8, lr: 1.120e-03, size: 512, ETA: 2:01:15
2025-08-01 05:58:00.161 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.119e-03, size: 288, ETA: 2:01:11
2025-08-01 05:58:03.456 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.118e-03, size: 288, ETA: 2:01:07
2025-08-01 05:58:06.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.117e-03, size: 512, ETA: 2:01:03
2025-08-01 05:58:08.422 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:58:15.049 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:58:15.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:58:15.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4021
2025-08-01 05:58:15.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3414
2025-08-01 05:58:15.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1675
2025-08-01 05:58:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3037
2025-08-01 05:58:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:58:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:58:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 05:58:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-08-01 05:58:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.168
2025-08-01 05:58:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.304
2025-08-01 05:58:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:58:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:58:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:58:15.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:58:15.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:58:15.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:58:15.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:58:15.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:58:15.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:58:16.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:58:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:58:16.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:58:17.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:58:17.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:58:17.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:58:17.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:58:18.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:58:18.568 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:58:18.568 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 05:58:18.569 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 05:58:18.569 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:58:18.574 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.69 ms, Average inference time: 8.07 ms

2025-08-01 05:58:18.575 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:58:18.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:58:18.731 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch284
2025-08-01 05:58:22.016 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 1.116e-03, size: 288, ETA: 2:00:57
2025-08-01 05:58:25.251 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 1.115e-03, size: 320, ETA: 2:00:53
2025-08-01 05:58:28.605 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 1.114e-03, size: 448, ETA: 2:00:49
2025-08-01 05:58:31.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 1.114e-03, size: 416, ETA: 2:00:46
2025-08-01 05:58:35.490 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 1.113e-03, size: 384, ETA: 2:00:42
2025-08-01 05:58:38.859 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.112e-03, size: 576, ETA: 2:00:38
2025-08-01 05:58:40.490 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:58:47.129 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:58:47.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:58:47.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4607
2025-08-01 05:58:48.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3833
2025-08-01 05:58:48.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2586
2025-08-01 05:58:48.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3676
2025-08-01 05:58:48.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:58:48.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:58:48.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-08-01 05:58:48.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-01 05:58:48.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.259
2025-08-01 05:58:48.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.368
2025-08-01 05:58:48.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:58:48.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:58:48.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:58:48.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:58:48.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:58:48.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:58:48.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:58:48.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:58:48.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:58:48.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:58:48.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:58:49.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:58:49.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:58:50.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:58:50.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:58:51.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:58:51.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:58:51.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:58:51.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 05:58:51.943 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 05:58:51.943 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:58:51.949 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.81 ms, Average inference time: 8.29 ms

2025-08-01 05:58:51.950 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:58:52.075 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:58:52.155 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch285
2025-08-01 05:58:55.586 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 1.111e-03, size: 384, ETA: 2:00:33
2025-08-01 05:58:58.823 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.110e-03, size: 320, ETA: 2:00:29
2025-08-01 05:59:02.009 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.6, lr: 1.109e-03, size: 416, ETA: 2:00:25
2025-08-01 05:59:05.152 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.108e-03, size: 320, ETA: 2:00:21
2025-08-01 05:59:08.568 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.5, lr: 1.107e-03, size: 576, ETA: 2:00:17
2025-08-01 05:59:11.965 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.7, lr: 1.107e-03, size: 480, ETA: 2:00:13
2025-08-01 05:59:13.428 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:59:20.072 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:59:21.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:59:21.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4761
2025-08-01 05:59:22.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4158
2025-08-01 05:59:22.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2871
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3930
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.393
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:59:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:59:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:59:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:59:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:59:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:59:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:59:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:59:22.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:59:23.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:59:24.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 05:59:25.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 05:59:26.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 05:59:27.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 05:59:27.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 05:59:28.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 05:59:29.681 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 05:59:29.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 05:59:29.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 05:59:29.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 05:59:29.690 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.85 ms, Average inference time: 8.29 ms

2025-08-01 05:59:29.691 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:59:29.768 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:59:29.854 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch286
2025-08-01 05:59:33.032 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.105e-03, size: 416, ETA: 2:00:07
2025-08-01 05:59:36.333 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, 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: 1.105e-03, size: 512, ETA: 2:00:03
2025-08-01 05:59:39.818 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.104e-03, size: 320, ETA: 2:00:00
2025-08-01 05:59:43.079 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 1.103e-03, size: 352, ETA: 1:59:56
2025-08-01 05:59:46.207 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 1.0, lr: 1.102e-03, size: 256, ETA: 1:59:52
2025-08-01 05:59:49.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 4.3, cls_loss: 0.9, lr: 1.101e-03, size: 480, ETA: 1:59:48
2025-08-01 05:59:51.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 05:59:57.831 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 05:59:58.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 05:59:58.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4752
2025-08-01 05:59:58.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4238
2025-08-01 05:59:58.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1405
2025-08-01 05:59:58.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3465
2025-08-01 05:59:58.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 05:59:58.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 05:59:58.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-01 05:59:58.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 05:59:58.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.140
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.347
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 05:59:58.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 05:59:59.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 05:59:59.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 05:59:59.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:00:00.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:00:00.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:00:01.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:00:01.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:00:02.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:00:02.443 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:00:02.443 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:00:02.443 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 06:00:02.443 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:00:02.451 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.73 ms, Average inference time: 8.24 ms

2025-08-01 06:00:02.452 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:00:02.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:00:02.616 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch287
2025-08-01 06:00:05.882 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 1.100e-03, size: 352, ETA: 1:59:42
2025-08-01 06:00:09.334 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.8, lr: 1.099e-03, size: 544, ETA: 1:59:39
2025-08-01 06:00:12.931 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.9, lr: 1.099e-03, size: 288, ETA: 1:59:35
2025-08-01 06:00:16.111 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 1.098e-03, size: 288, ETA: 1:59:31
2025-08-01 06:00:19.592 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.097e-03, size: 512, ETA: 1:59:27
2025-08-01 06:00:22.906 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.8, lr: 1.096e-03, size: 256, ETA: 1:59:23
2025-08-01 06:00:24.323 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:00:31.029 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:00:32.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:00:32.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3585
2025-08-01 06:00:32.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3408
2025-08-01 06:00:32.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1734
2025-08-01 06:00:32.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2909
2025-08-01 06:00:32.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:00:32.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.173
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.291
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:00:32.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:00:32.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:00:33.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:00:34.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:00:35.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:00:35.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:00:36.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:00:37.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:00:37.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:00:38.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:00:39.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:00:39.352 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 06:00:39.352 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 06:00:39.352 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:00:39.359 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.82 ms, Average inference time: 8.21 ms

2025-08-01 06:00:39.360 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:00:39.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:00:39.570 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch288
2025-08-01 06:00:42.879 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.6, lr: 1.095e-03, size: 576, ETA: 1:59:18
2025-08-01 06:00:46.420 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.7, lr: 1.094e-03, size: 288, ETA: 1:59:14
2025-08-01 06:00:49.673 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.093e-03, size: 352, ETA: 1:59:10
2025-08-01 06:00:53.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 1.092e-03, size: 384, ETA: 1:59:06
2025-08-01 06:00:56.354 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 1.092e-03, size: 256, ETA: 1:59:02
2025-08-01 06:00:59.553 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.8, lr: 1.091e-03, size: 384, ETA: 1:58:58
2025-08-01 06:01:01.051 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:01:07.738 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:01:08.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:01:08.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4040
2025-08-01 06:01:09.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3595
2025-08-01 06:01:09.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1550
2025-08-01 06:01:09.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3062
2025-08-01 06:01:09.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.155
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.306
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:01:09.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:01:09.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:01:09.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:01:09.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:01:09.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:01:10.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:01:10.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:01:11.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:01:12.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:01:12.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:01:13.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:01:13.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:01:14.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:01:14.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 06:01:14.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 06:01:14.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:01:14.353 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.81 ms, Average inference time: 8.32 ms

2025-08-01 06:01:14.354 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:01:14.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:01:14.513 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch289
2025-08-01 06:01:17.783 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.6, lr: 1.090e-03, size: 352, ETA: 1:58:53
2025-08-01 06:01:21.042 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.7, lr: 1.089e-03, size: 544, ETA: 1:58:49
2025-08-01 06:01:24.513 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.088e-03, size: 320, ETA: 1:58:45
2025-08-01 06:01:28.171 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 1.087e-03, size: 256, ETA: 1:58:42
2025-08-01 06:01:31.549 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.7, lr: 1.086e-03, size: 448, ETA: 1:58:38
2025-08-01 06:01:34.868 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 1.085e-03, size: 352, ETA: 1:58:34
2025-08-01 06:01:36.337 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:01:43.016 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:01:44.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:01:44.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3841
2025-08-01 06:01:44.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3013
2025-08-01 06:01:44.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1350
2025-08-01 06:01:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2735
2025-08-01 06:01:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:01:44.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:01:44.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-01 06:01:44.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-08-01 06:01:44.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.135
2025-08-01 06:01:44.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.273
2025-08-01 06:01:44.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:01:44.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:01:44.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:01:44.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:01:44.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:01:44.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:01:44.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:01:44.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:01:44.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:01:45.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:01:46.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:01:47.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:01:48.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:01:49.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:01:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:01:50.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:01:51.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:01:52.450 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:01:52.450 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 06:01:52.450 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 06:01:52.450 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:01:52.459 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.89 ms, Average inference time: 8.31 ms

2025-08-01 06:01:52.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:01:52.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:01:52.619 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch290
2025-08-01 06:01:55.699 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.084e-03, size: 256, ETA: 1:58:28
2025-08-01 06:01:58.966 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.8, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.6, cls_loss: 0.7, lr: 1.083e-03, size: 256, ETA: 1:58:24
2025-08-01 06:02:02.196 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.083e-03, size: 480, ETA: 1:58:20
2025-08-01 06:02:05.626 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.082e-03, size: 320, ETA: 1:58:16
2025-08-01 06:02:08.907 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.081e-03, size: 480, ETA: 1:58:12
2025-08-01 06:02:12.454 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, 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: 1.080e-03, size: 384, ETA: 1:58:09
2025-08-01 06:02:13.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:02:20.642 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:02:21.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:02:21.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4594
2025-08-01 06:02:22.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4206
2025-08-01 06:02:22.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2488
2025-08-01 06:02:22.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3763
2025-08-01 06:02:22.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:02:22.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:02:22.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-08-01 06:02:22.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-08-01 06:02:22.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 06:02:22.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-08-01 06:02:22.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:02:22.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:02:22.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:02:22.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:02:22.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:02:22.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:02:22.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:02:22.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:02:22.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:02:22.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:02:23.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:02:23.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:02:24.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:02:24.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:02:25.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:02:25.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:02:26.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:02:26.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:02:26.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:02:26.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 06:02:26.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:02:26.915 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.65 ms, Average NMS time: 0.81 ms, Average inference time: 8.46 ms

2025-08-01 06:02:26.917 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:02:26.994 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:02:27.073 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch291
2025-08-01 06:02:30.389 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.079e-03, size: 544, ETA: 1:58:03
2025-08-01 06:02:33.657 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.078e-03, size: 384, ETA: 1:57:59
2025-08-01 06:02:37.048 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.077e-03, size: 544, ETA: 1:57:55
2025-08-01 06:02:40.467 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.5, lr: 1.076e-03, size: 544, ETA: 1:57:52
2025-08-01 06:02:43.788 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 1.076e-03, size: 448, ETA: 1:57:48
2025-08-01 06:02:47.337 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.8, lr: 1.075e-03, size: 544, ETA: 1:57:44
2025-08-01 06:02:48.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:02:55.483 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:02:57.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:02:58.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4873
2025-08-01 06:02:58.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3920
2025-08-01 06:02:58.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2448
2025-08-01 06:02:58.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3747
2025-08-01 06:02:58.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:02:58.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:02:58.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.245
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.375
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:02:58.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:03:00.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:03:01.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:03:02.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:03:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:03:05.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:03:06.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:03:07.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:03:08.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:03:09.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:03:09.775 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:03:09.775 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 06:03:09.775 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:03:09.783 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.85 ms, Average inference time: 8.32 ms

2025-08-01 06:03:09.786 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:03:09.863 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:03:09.944 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch292
2025-08-01 06:03:13.195 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.074e-03, size: 512, ETA: 1:57:39
2025-08-01 06:03:16.621 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.073e-03, size: 544, ETA: 1:57:35
2025-08-01 06:03:20.142 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.072e-03, size: 576, ETA: 1:57:31
2025-08-01 06:03:23.572 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.005s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 1.4, cls_loss: 0.6, lr: 1.071e-03, size: 480, ETA: 1:57:27
2025-08-01 06:03:26.928 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 1.070e-03, size: 320, ETA: 1:57:24
2025-08-01 06:03:30.441 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.8, lr: 1.070e-03, size: 384, ETA: 1:57:20
2025-08-01 06:03:31.947 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:03:38.540 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:03:39.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:03:39.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4116
2025-08-01 06:03:39.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4686
2025-08-01 06:03:39.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2031
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3611
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.203
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:03:39.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:03:39.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:03:39.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:03:39.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:03:39.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:03:39.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:03:39.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:03:39.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:03:40.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:03:40.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:03:41.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:03:41.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:03:42.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:03:42.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:03:43.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:03:43.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:03:43.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:03:43.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:03:43.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:03:43.502 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.74 ms, Average inference time: 8.16 ms

2025-08-01 06:03:43.504 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:03:43.585 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:03:43.663 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch293
2025-08-01 06:03:46.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 3.1, cls_loss: 0.9, lr: 1.068e-03, size: 384, ETA: 1:57:14
2025-08-01 06:03:50.263 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.068e-03, size: 288, ETA: 1:57:11
2025-08-01 06:03:53.455 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 1.067e-03, size: 384, ETA: 1:57:07
2025-08-01 06:03:56.830 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.066e-03, size: 448, ETA: 1:57:03
2025-08-01 06:04:00.165 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 9.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 4.4, cls_loss: 0.8, lr: 1.065e-03, size: 480, ETA: 1:56:59
2025-08-01 06:04:03.448 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 1.064e-03, size: 320, ETA: 1:56:55
2025-08-01 06:04:04.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:04:11.540 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:04:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:04:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5093
2025-08-01 06:04:12.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4656
2025-08-01 06:04:12.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3207
2025-08-01 06:04:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4319
2025-08-01 06:04:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:04:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:04:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:04:12.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:04:12.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:04:13.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:04:14.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:04:14.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:04:15.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:04:15.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:04:16.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:04:17.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:04:17.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:04:18.323 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:04:18.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 06:04:18.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 06:04:18.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:04:18.333 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.81 ms, Average inference time: 8.34 ms

2025-08-01 06:04:18.334 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:04:18.475 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:04:18.601 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch294
2025-08-01 06:04:21.952 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.9, lr: 1.063e-03, size: 512, ETA: 1:56:50
2025-08-01 06:04:25.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 20.3, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 20.3, cls_loss: 0.0, lr: 1.062e-03, size: 256, ETA: 1:56:46
2025-08-01 06:04:28.741 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 1.1, lr: 1.061e-03, size: 352, ETA: 1:56:42
2025-08-01 06:04:32.126 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.3, l1_loss: 1.6, conf_loss: 2.4, cls_loss: 0.8, lr: 1.061e-03, size: 480, ETA: 1:56:38
2025-08-01 06:04:35.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 1.060e-03, size: 416, ETA: 1:56:34
2025-08-01 06:04:38.707 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.059e-03, size: 384, ETA: 1:56:31
2025-08-01 06:04:40.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:04:46.911 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:04:47.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:04:48.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5068
2025-08-01 06:04:48.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4291
2025-08-01 06:04:48.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2655
2025-08-01 06:04:48.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4005
2025-08-01 06:04:48.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:04:48.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:04:48.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-01 06:04:48.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-01 06:04:48.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-08-01 06:04:48.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.400
2025-08-01 06:04:48.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:04:48.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:04:48.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:04:48.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:04:48.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:04:48.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:04:48.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:04:48.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:04:48.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:04:49.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:04:49.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:04:50.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:04:51.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:04:51.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:04:52.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:04:52.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:04:53.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:04:53.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:04:53.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:04:53.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 06:04:53.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:04:53.927 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.82 ms, Average inference time: 8.27 ms

2025-08-01 06:04:53.929 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:04:54.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:04:54.096 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch295
2025-08-01 06:04:57.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 1.058e-03, size: 320, ETA: 1:56:25
2025-08-01 06:05:01.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.057e-03, size: 544, ETA: 1:56:22
2025-08-01 06:05:04.868 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 1.056e-03, size: 576, ETA: 1:56:18
2025-08-01 06:05:08.305 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.055e-03, size: 416, ETA: 1:56:14
2025-08-01 06:05:11.802 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.2, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.9, lr: 1.054e-03, size: 256, ETA: 1:56:11
2025-08-01 06:05:15.103 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.054e-03, size: 512, ETA: 1:56:07
2025-08-01 06:05:16.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:05:23.589 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:05:24.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:05:24.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4134
2025-08-01 06:05:24.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3588
2025-08-01 06:05:24.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1839
2025-08-01 06:05:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3187
2025-08-01 06:05:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:05:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:05:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 06:05:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 06:05:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-08-01 06:05:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.319
2025-08-01 06:05:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:05:24.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:05:24.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:05:24.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:05:24.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:05:24.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:05:24.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:05:24.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:05:24.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:05:24.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:05:25.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:05:25.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:05:26.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:05:26.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:05:27.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:05:27.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:05:27.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:05:28.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:05:28.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 06:05:28.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 06:05:28.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:05:28.411 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.76 ms, Average inference time: 8.22 ms

2025-08-01 06:05:28.412 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:05:28.529 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:05:28.644 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch296
2025-08-01 06:05:31.861 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.8, lr: 1.052e-03, size: 288, ETA: 1:56:01
2025-08-01 06:05:35.315 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.052e-03, size: 480, ETA: 1:55:58
2025-08-01 06:05:38.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.9, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.5, lr: 1.051e-03, size: 384, ETA: 1:55:54
2025-08-01 06:05:42.129 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.050e-03, size: 352, ETA: 1:55:50
2025-08-01 06:05:45.668 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 2.6, cls_loss: 0.8, lr: 1.049e-03, size: 416, ETA: 1:55:46
2025-08-01 06:05:49.077 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.9, cls_loss: 0.7, lr: 1.048e-03, size: 352, ETA: 1:55:43
2025-08-01 06:05:50.608 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:05:57.200 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:05:58.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:05:58.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4813
2025-08-01 06:05:58.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3587
2025-08-01 06:05:58.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2436
2025-08-01 06:05:58.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3612
2025-08-01 06:05:58.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:05:58.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:05:58.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 06:05:58.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 06:05:58.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-08-01 06:05:58.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:05:58.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:05:59.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:05:59.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:06:00.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:06:00.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:06:01.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:06:02.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:06:02.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:06:03.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:06:03.793 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:06:03.793 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:06:03.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:06:03.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:06:03.801 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.82 ms, Average inference time: 8.21 ms

2025-08-01 06:06:03.803 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:06:03.887 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:06:03.969 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch297
2025-08-01 06:06:07.338 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.047e-03, size: 256, ETA: 1:55:37
2025-08-01 06:06:10.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 1.046e-03, size: 480, ETA: 1:55:33
2025-08-01 06:06:14.000 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.8, lr: 1.045e-03, size: 256, ETA: 1:55:30
2025-08-01 06:06:17.353 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.005s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.045e-03, size: 288, ETA: 1:55:26
2025-08-01 06:06:20.675 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, 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: 1.044e-03, size: 288, ETA: 1:55:22
2025-08-01 06:06:23.841 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.8, lr: 1.043e-03, size: 352, ETA: 1:55:18
2025-08-01 06:06:25.288 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:06:32.117 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:06:32.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:06:33.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2696
2025-08-01 06:06:33.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2575
2025-08-01 06:06:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1443
2025-08-01 06:06:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2238
2025-08-01 06:06:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:06:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:06:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-08-01 06:06:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-08-01 06:06:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.144
2025-08-01 06:06:33.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.224
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:06:33.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:06:34.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:06:34.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:06:35.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:06:35.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:06:36.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:06:36.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:06:37.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:06:37.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:06:38.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:06:38.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 06:06:38.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-08-01 06:06:38.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:06:38.257 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.84 ms, Average inference time: 8.42 ms

2025-08-01 06:06:38.258 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:06:38.337 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:06:38.422 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch298
2025-08-01 06:06:41.857 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.7, lr: 1.042e-03, size: 448, ETA: 1:55:13
2025-08-01 06:06:45.298 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.9, lr: 1.041e-03, size: 416, ETA: 1:55:09
2025-08-01 06:06:48.695 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 10.1, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 5.2, cls_loss: 0.7, lr: 1.040e-03, size: 384, ETA: 1:55:05
2025-08-01 06:06:52.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.005s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.039e-03, size: 480, ETA: 1:55:01
2025-08-01 06:06:55.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.038e-03, size: 288, ETA: 1:54:58
2025-08-01 06:06:58.844 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, 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: 1.038e-03, size: 512, ETA: 1:54:54
2025-08-01 06:07:00.415 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:07:07.050 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:07:07.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:07:08.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4832
2025-08-01 06:07:08.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3883
2025-08-01 06:07:08.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2301
2025-08-01 06:07:08.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3672
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.230
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.367
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:07:08.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:07:08.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:07:08.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:07:08.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:07:09.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:07:10.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:07:10.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:07:11.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:07:11.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:07:12.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:07:13.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:07:13.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:07:13.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 06:07:13.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 06:07:13.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:07:13.610 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.80 ms, Average inference time: 8.19 ms

2025-08-01 06:07:13.614 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:07:13.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:07:13.777 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch299
2025-08-01 06:07:17.153 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.4, l1_loss: 0.9, conf_loss: 4.1, cls_loss: 0.9, lr: 1.036e-03, size: 576, ETA: 1:54:48
2025-08-01 06:07:20.878 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 1.036e-03, size: 288, ETA: 1:54:45
2025-08-01 06:07:24.152 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 1.035e-03, size: 512, ETA: 1:54:41
2025-08-01 06:07:27.500 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, 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: 1.034e-03, size: 544, ETA: 1:54:37
2025-08-01 06:07:30.864 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.8, lr: 1.033e-03, size: 288, ETA: 1:54:34
2025-08-01 06:07:34.094 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.9, lr: 1.032e-03, size: 256, ETA: 1:54:30
2025-08-01 06:07:35.493 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:07:42.269 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:07:43.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:07:43.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2921
2025-08-01 06:07:43.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2689
2025-08-01 06:07:44.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1314
2025-08-01 06:07:44.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2308
2025-08-01 06:07:44.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.131
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.231
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:07:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:07:44.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:07:44.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:07:44.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:07:44.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:07:44.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:07:44.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:07:45.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:07:46.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:07:47.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:07:48.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:07:48.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:07:49.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:07:50.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:07:51.081 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:07:51.081 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 06:07:51.081 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-08-01 06:07:51.081 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:07:51.089 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.85 ms, Average inference time: 8.32 ms

2025-08-01 06:07:51.091 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:07:51.174 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:07:51.256 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch300
2025-08-01 06:07:54.658 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.031e-03, size: 448, ETA: 1:54:24
2025-08-01 06:07:58.110 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.030e-03, size: 256, ETA: 1:54:20
2025-08-01 06:08:01.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 1.029e-03, size: 576, ETA: 1:54:17
2025-08-01 06:08:04.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, 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: 1.029e-03, size: 384, ETA: 1:54:13
2025-08-01 06:08:08.294 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.8, lr: 1.028e-03, size: 480, ETA: 1:54:09
2025-08-01 06:08:11.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.4, l1_loss: 0.3, conf_loss: 1.2, cls_loss: 0.5, lr: 1.027e-03, size: 256, ETA: 1:54:05
2025-08-01 06:08:13.132 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:08:19.992 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:08:21.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:08:22.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4571
2025-08-01 06:08:23.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3617
2025-08-01 06:08:23.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2131
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3440
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:08:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:08:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:08:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:08:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:08:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:08:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:08:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:08:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:08:24.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:08:26.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:08:27.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:08:29.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:08:30.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:08:32.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:08:33.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:08:35.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:08:36.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:08:36.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:08:36.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 06:08:36.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:08:36.932 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.87 ms, Average inference time: 8.40 ms

2025-08-01 06:08:36.933 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:08:37.049 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:08:37.168 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch301
2025-08-01 06:08:40.545 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 301/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.6, lr: 1.026e-03, size: 384, ETA: 1:54:00
2025-08-01 06:08:43.808 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 301/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.025e-03, size: 384, ETA: 1:53:56
2025-08-01 06:08:47.184 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 301/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 0.7, lr: 1.024e-03, size: 512, ETA: 1:53:52
2025-08-01 06:08:50.845 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 301/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, 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: 1.023e-03, size: 576, ETA: 1:53:49
2025-08-01 06:08:54.071 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 301/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.022e-03, size: 256, ETA: 1:53:45
2025-08-01 06:08:57.554 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 301/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.8, lr: 1.022e-03, size: 512, ETA: 1:53:41
2025-08-01 06:08:59.108 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:09:05.731 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:09:06.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:09:06.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4856
2025-08-01 06:09:06.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4306
2025-08-01 06:09:06.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2640
2025-08-01 06:09:06.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3934
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.393
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:09:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:09:06.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:09:06.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:09:06.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:09:06.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:09:07.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:09:08.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:09:08.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:09:09.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:09:09.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:09:10.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:09:10.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:09:11.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:09:11.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:09:11.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:09:11.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 06:09:11.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:09:11.970 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.83 ms, Average inference time: 8.27 ms

2025-08-01 06:09:11.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:09:12.092 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:09:12.178 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch302
2025-08-01 06:09:15.440 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 302/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.7, lr: 1.020e-03, size: 512, ETA: 1:53:36
2025-08-01 06:09:18.722 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 302/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, 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: 1.020e-03, size: 448, ETA: 1:53:32
2025-08-01 06:09:22.216 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 302/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 1.019e-03, size: 384, ETA: 1:53:28
2025-08-01 06:09:25.601 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 302/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, 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: 1.018e-03, size: 416, ETA: 1:53:24
2025-08-01 06:09:28.824 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 302/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 1.017e-03, size: 384, ETA: 1:53:20
2025-08-01 06:09:32.175 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 302/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.9, lr: 1.016e-03, size: 544, ETA: 1:53:17
2025-08-01 06:09:33.626 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:09:40.453 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:09:41.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:09:42.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5117
2025-08-01 06:09:42.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4305
2025-08-01 06:09:42.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3347
2025-08-01 06:09:42.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4257
2025-08-01 06:09:42.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:09:42.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:09:42.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.335
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:09:42.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:09:42.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:09:43.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:09:44.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:09:45.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:09:46.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:09:47.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:09:48.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:09:49.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:09:50.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:09:51.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:09:51.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:09:51.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 06:09:51.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:09:51.268 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.82 ms, Average inference time: 8.28 ms

2025-08-01 06:09:51.269 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:09:51.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:09:51.435 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch303
2025-08-01 06:09:54.454 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 303/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.015e-03, size: 320, ETA: 1:53:11
2025-08-01 06:09:57.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 303/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.8, lr: 1.014e-03, size: 352, ETA: 1:53:07
2025-08-01 06:10:01.060 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 303/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 9.0, iou_loss: 3.3, l1_loss: 1.4, conf_loss: 3.7, cls_loss: 0.7, lr: 1.013e-03, size: 448, ETA: 1:53:03
2025-08-01 06:10:04.445 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 303/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 1.013e-03, size: 480, ETA: 1:52:59
2025-08-01 06:10:07.978 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 303/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.012e-03, size: 288, ETA: 1:52:56
2025-08-01 06:10:11.371 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 303/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.011e-03, size: 320, ETA: 1:52:52
2025-08-01 06:10:12.849 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:10:19.632 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:10:20.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:10:20.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4510
2025-08-01 06:10:20.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3843
2025-08-01 06:10:20.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2912
2025-08-01 06:10:20.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3755
2025-08-01 06:10:20.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:10:20.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:10:20.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 06:10:20.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-01 06:10:20.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.291
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:10:20.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:10:21.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:10:21.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:10:22.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:10:22.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:10:23.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:10:23.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:10:24.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:10:24.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:10:25.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:10:25.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:10:25.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 06:10:25.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:10:25.352 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.59 ms, Average NMS time: 0.84 ms, Average inference time: 8.43 ms

2025-08-01 06:10:25.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:10:25.451 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:10:25.549 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch304
2025-08-01 06:10:28.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 304/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.8, lr: 1.010e-03, size: 512, ETA: 1:52:46
2025-08-01 06:10:32.290 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 304/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.6, lr: 1.009e-03, size: 512, ETA: 1:52:43
2025-08-01 06:10:35.696 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 304/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 1.008e-03, size: 320, ETA: 1:52:39
2025-08-01 06:10:39.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 304/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, 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: 1.007e-03, size: 384, ETA: 1:52:35
2025-08-01 06:10:42.446 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 304/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.9, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.6, lr: 1.007e-03, size: 512, ETA: 1:52:31
2025-08-01 06:10:45.894 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 304/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.8, lr: 1.006e-03, size: 576, ETA: 1:52:28
2025-08-01 06:10:47.448 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:10:54.133 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:10:54.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:10:55.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4154
2025-08-01 06:10:55.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3028
2025-08-01 06:10:55.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1421
2025-08-01 06:10:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2867
2025-08-01 06:10:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:10:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:10:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-01 06:10:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-01 06:10:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.142
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.287
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:10:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:10:55.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:10:55.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:10:56.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:10:56.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:10:57.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:10:57.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:10:58.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:10:58.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:10:59.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:10:59.955 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:10:59.955 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 06:10:59.955 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 06:10:59.955 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:10:59.962 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.81 ms, Average inference time: 8.16 ms

2025-08-01 06:10:59.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:11:00.053 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:11:00.133 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch305
2025-08-01 06:11:03.350 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 305/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 1.004e-03, size: 448, ETA: 1:52:22
2025-08-01 06:11:06.764 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 305/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, 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: 1.004e-03, size: 256, ETA: 1:52:18
2025-08-01 06:11:10.137 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 305/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 1.003e-03, size: 576, ETA: 1:52:15
2025-08-01 06:11:13.524 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 305/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 1.002e-03, size: 384, ETA: 1:52:11
2025-08-01 06:11:17.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 305/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 10.3, iou_loss: 3.6, l1_loss: 1.4, conf_loss: 4.3, cls_loss: 1.0, lr: 1.001e-03, size: 480, ETA: 1:52:07
2025-08-01 06:11:20.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 305/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.000e-03, size: 448, ETA: 1:52:04
2025-08-01 06:11:22.033 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:11:28.709 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:11:29.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:11:29.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4407
2025-08-01 06:11:29.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3742
2025-08-01 06:11:29.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2187
2025-08-01 06:11:29.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3445
2025-08-01 06:11:29.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:11:29.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:11:29.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.219
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:11:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:11:29.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:11:29.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:11:29.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:11:30.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:11:30.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:11:31.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:11:31.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:11:31.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:11:32.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:11:32.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:11:33.224 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:11:33.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:11:33.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 06:11:33.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:11:33.233 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.77 ms, Average inference time: 8.16 ms

2025-08-01 06:11:33.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:11:33.354 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:11:33.434 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch306
2025-08-01 06:11:36.537 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 306/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, 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: 9.992e-04, size: 352, ETA: 1:51:58
2025-08-01 06:11:39.819 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 306/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 9.983e-04, size: 416, ETA: 1:51:54
2025-08-01 06:11:43.344 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 306/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 9.975e-04, size: 384, ETA: 1:51:51
2025-08-01 06:11:46.812 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 306/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, 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: 9.967e-04, size: 544, ETA: 1:51:47
2025-08-01 06:11:50.158 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 306/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.8, lr: 9.959e-04, size: 416, ETA: 1:51:43
2025-08-01 06:11:53.604 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 306/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.3, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.7, lr: 9.950e-04, size: 288, ETA: 1:51:39
2025-08-01 06:11:55.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:12:01.693 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:12:02.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:12:03.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5261
2025-08-01 06:12:03.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4442
2025-08-01 06:12:03.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3411
2025-08-01 06:12:03.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4371
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:12:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:12:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:12:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:12:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:12:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:12:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:12:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:12:03.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:12:04.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:12:05.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:12:05.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:12:06.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:12:07.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:12:08.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:12:08.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:12:09.316 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:12:09.316 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 06:12:09.317 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-01 06:12:09.317 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:12:09.324 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.82 ms, Average inference time: 8.27 ms

2025-08-01 06:12:09.326 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:12:09.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:12:09.490 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch307
2025-08-01 06:12:12.731 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 307/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 9.938e-04, size: 416, ETA: 1:51:34
2025-08-01 06:12:15.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 307/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.8, lr: 9.930e-04, size: 320, ETA: 1:51:30
2025-08-01 06:12:19.154 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 307/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, 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: 9.922e-04, size: 384, ETA: 1:51:26
2025-08-01 06:12:22.605 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 307/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.006s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.914e-04, size: 576, ETA: 1:51:22
2025-08-01 06:12:26.028 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 307/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 9.905e-04, size: 448, ETA: 1:51:18
2025-08-01 06:12:29.476 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 307/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 9.897e-04, size: 256, ETA: 1:51:15
2025-08-01 06:12:30.931 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:12:37.560 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:12:38.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:12:39.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4562
2025-08-01 06:12:39.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4338
2025-08-01 06:12:39.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2949
2025-08-01 06:12:39.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3950
2025-08-01 06:12:39.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:12:39.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:12:39.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-01 06:12:39.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-08-01 06:12:39.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-01 06:12:39.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-08-01 06:12:39.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:12:39.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:12:39.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:12:39.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:12:39.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:12:39.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:12:39.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:12:39.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:12:39.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:12:40.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:12:40.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:12:41.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:12:42.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:12:43.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:12:43.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:12:44.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:12:45.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:12:46.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:12:46.194 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:12:46.194 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 06:12:46.194 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:12:46.202 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.86 ms, Average inference time: 8.43 ms

2025-08-01 06:12:46.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:12:46.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:12:46.442 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch308
2025-08-01 06:12:49.762 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 308/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 9.885e-04, size: 544, ETA: 1:51:09
2025-08-01 06:12:53.115 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 308/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.7, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 9.877e-04, size: 256, ETA: 1:51:05
2025-08-01 06:12:56.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 308/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 9.869e-04, size: 256, ETA: 1:51:02
2025-08-01 06:13:00.098 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 308/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.860e-04, size: 448, ETA: 1:50:58
2025-08-01 06:13:03.397 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 308/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.9, lr: 9.852e-04, size: 256, ETA: 1:50:54
2025-08-01 06:13:06.656 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 308/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 9.844e-04, size: 544, ETA: 1:50:50
2025-08-01 06:13:08.197 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:13:14.925 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:13:15.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:13:15.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5125
2025-08-01 06:13:15.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4092
2025-08-01 06:13:15.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3040
2025-08-01 06:13:15.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4086
2025-08-01 06:13:15.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:13:15.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:13:15.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-01 06:13:15.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-08-01 06:13:15.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:13:15.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:13:16.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:13:16.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:13:17.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:13:17.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:13:17.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:13:18.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:13:18.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:13:19.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:13:19.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:13:19.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:13:19.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:13:19.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:13:19.632 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.76 ms, Average inference time: 8.26 ms

2025-08-01 06:13:19.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:13:19.714 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:13:19.793 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch309
2025-08-01 06:13:22.898 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 309/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 3.4, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.0, lr: 9.832e-04, size: 576, ETA: 1:50:45
2025-08-01 06:13:26.363 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 309/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.4, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.8, lr: 9.824e-04, size: 448, ETA: 1:50:41
2025-08-01 06:13:29.760 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 309/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.8, lr: 9.816e-04, size: 384, ETA: 1:50:37
2025-08-01 06:13:33.221 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 309/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 0.8, lr: 9.807e-04, size: 256, ETA: 1:50:34
2025-08-01 06:13:36.565 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 309/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 9.799e-04, size: 576, ETA: 1:50:30
2025-08-01 06:13:40.069 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 309/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 9.791e-04, size: 256, ETA: 1:50:26
2025-08-01 06:13:41.475 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:13:48.193 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:13:49.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:13:50.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4123
2025-08-01 06:13:50.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3976
2025-08-01 06:13:50.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1578
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3225
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.158
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.323
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:13:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:13:50.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:13:50.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:13:50.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:13:50.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:13:50.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:13:50.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:13:52.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:13:53.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:13:54.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:13:55.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:13:56.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:13:57.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:13:58.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:13:59.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:14:00.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:14:00.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:14:00.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 06:14:00.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:14:00.425 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.82 ms, Average inference time: 8.27 ms

2025-08-01 06:14:00.426 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:14:00.548 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:14:00.641 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch310
2025-08-01 06:14:03.959 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 310/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 9.779e-04, size: 352, ETA: 1:50:21
2025-08-01 06:14:07.215 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 310/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 9.771e-04, size: 256, ETA: 1:50:17
2025-08-01 06:14:10.718 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 310/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 9.762e-04, size: 416, ETA: 1:50:13
2025-08-01 06:14:14.187 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 310/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 9.754e-04, size: 480, ETA: 1:50:10
2025-08-01 06:14:17.523 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 310/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 9.746e-04, size: 544, ETA: 1:50:06
2025-08-01 06:14:20.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 310/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.8, lr: 9.738e-04, size: 256, ETA: 1:50:02
2025-08-01 06:14:22.364 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:14:28.976 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:14:30.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:14:31.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3788
2025-08-01 06:14:31.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2852
2025-08-01 06:14:31.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1551
2025-08-01 06:14:31.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2730
2025-08-01 06:14:31.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:14:31.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:14:31.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-01 06:14:31.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-08-01 06:14:31.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.155
2025-08-01 06:14:31.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.273
2025-08-01 06:14:31.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:14:31.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:14:31.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:14:31.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:14:31.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:14:31.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:14:31.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:14:31.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:14:31.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:14:33.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:14:34.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:14:35.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:14:36.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:14:38.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:14:39.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:14:40.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:14:42.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:14:43.394 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:14:43.394 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 06:14:43.394 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 06:14:43.394 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:14:43.401 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.84 ms, Average inference time: 8.18 ms

2025-08-01 06:14:43.403 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:14:43.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:14:43.560 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch311
2025-08-01 06:14:46.614 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 311/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 9.726e-04, size: 320, ETA: 1:49:56
2025-08-01 06:14:49.795 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 311/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 9.717e-04, size: 352, ETA: 1:49:52
2025-08-01 06:14:53.412 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 311/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 9.709e-04, size: 544, ETA: 1:49:49
2025-08-01 06:14:56.983 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 311/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 10.6, iou_loss: 3.1, l1_loss: 1.5, conf_loss: 5.2, cls_loss: 0.9, lr: 9.701e-04, size: 544, ETA: 1:49:45
2025-08-01 06:15:00.309 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 311/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 9.693e-04, size: 384, ETA: 1:49:42
2025-08-01 06:15:03.679 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 311/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 9.684e-04, size: 352, ETA: 1:49:38
2025-08-01 06:15:05.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:15:11.913 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:15:12.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:15:12.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5005
2025-08-01 06:15:12.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4256
2025-08-01 06:15:12.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2666
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3976
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:15:12.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:15:12.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:15:12.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:15:12.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:15:12.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:15:12.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:15:12.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:15:12.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:15:13.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:15:13.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:15:14.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:15:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:15:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:15:15.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:15:16.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:15:16.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:15:17.264 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:15:17.264 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:15:17.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 06:15:17.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:15:17.278 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.80 ms, Average inference time: 8.24 ms

2025-08-01 06:15:17.279 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:15:17.391 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:15:17.487 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch312
2025-08-01 06:15:20.758 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 312/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.6, lr: 9.672e-04, size: 480, ETA: 1:49:32
2025-08-01 06:15:24.008 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 312/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 9.664e-04, size: 384, ETA: 1:49:29
2025-08-01 06:15:27.327 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 312/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 9.656e-04, size: 384, ETA: 1:49:25
2025-08-01 06:15:30.538 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 312/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 9.648e-04, size: 288, ETA: 1:49:21
2025-08-01 06:15:33.814 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 312/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 9.639e-04, size: 320, ETA: 1:49:17
2025-08-01 06:15:37.255 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 312/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 9.631e-04, size: 480, ETA: 1:49:14
2025-08-01 06:15:38.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:15:45.519 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:15:46.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:15:46.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4738
2025-08-01 06:15:46.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4194
2025-08-01 06:15:46.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2692
2025-08-01 06:15:46.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3875
2025-08-01 06:15:46.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:15:46.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:15:46.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-01 06:15:46.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-01 06:15:46.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:15:47.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:15:47.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:15:48.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:15:48.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:15:49.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:15:49.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:15:50.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:15:51.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:15:51.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:15:52.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:15:52.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:15:52.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 06:15:52.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:15:52.364 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.81 ms, Average inference time: 8.36 ms

2025-08-01 06:15:52.365 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:15:52.444 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:15:52.526 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch313
2025-08-01 06:15:55.711 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 313/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 9.619e-04, size: 512, ETA: 1:49:08
2025-08-01 06:15:59.068 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 313/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 9.611e-04, size: 384, ETA: 1:49:04
2025-08-01 06:16:02.392 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 313/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 9.603e-04, size: 448, ETA: 1:49:00
2025-08-01 06:16:05.601 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 313/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.157s, data_time: 0.005s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 9.594e-04, size: 256, ETA: 1:48:57
2025-08-01 06:16:08.990 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 313/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 9.2, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.6, cls_loss: 1.4, lr: 9.586e-04, size: 576, ETA: 1:48:53
2025-08-01 06:16:12.273 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 313/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.9, lr: 9.578e-04, size: 256, ETA: 1:48:49
2025-08-01 06:16:13.652 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:16:20.408 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:16:21.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:16:21.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4641
2025-08-01 06:16:21.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4177
2025-08-01 06:16:22.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2074
2025-08-01 06:16:22.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3631
2025-08-01 06:16:22.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:16:22.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:16:22.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-08-01 06:16:22.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-01 06:16:22.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.207
2025-08-01 06:16:22.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.363
2025-08-01 06:16:22.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:16:22.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:16:22.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:16:22.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:16:22.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:16:22.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:16:22.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:16:22.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:16:22.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:16:22.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:16:23.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:16:24.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:16:24.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:16:25.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:16:26.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:16:26.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:16:27.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:16:28.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:16:28.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:16:28.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:16:28.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:16:28.242 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.87 ms, Average inference time: 8.26 ms

2025-08-01 06:16:28.244 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:16:28.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:16:28.407 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch314
2025-08-01 06:16:31.563 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 314/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 9.566e-04, size: 320, ETA: 1:48:43
2025-08-01 06:16:35.166 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 314/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 9.558e-04, size: 544, ETA: 1:48:40
2025-08-01 06:16:38.484 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 314/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.8, lr: 9.549e-04, size: 416, ETA: 1:48:36
2025-08-01 06:16:41.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 314/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 9.541e-04, size: 512, ETA: 1:48:32
2025-08-01 06:16:45.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 314/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 9.533e-04, size: 512, ETA: 1:48:28
2025-08-01 06:16:48.507 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 314/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.9, lr: 9.525e-04, size: 576, ETA: 1:48:25
2025-08-01 06:16:50.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:16:56.839 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:16:57.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:16:58.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5218
2025-08-01 06:16:58.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4052
2025-08-01 06:16:58.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2736
2025-08-01 06:16:58.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4002
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.274
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.400
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:16:58.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:16:58.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:16:58.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:16:58.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:16:58.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:16:59.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:17:00.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:17:00.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:17:01.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:17:02.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:17:03.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:17:04.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:17:04.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:17:05.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:17:05.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:17:05.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 06:17:05.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:17:05.698 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.83 ms, Average inference time: 8.29 ms

2025-08-01 06:17:05.698 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:17:05.813 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:17:05.923 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch315
2025-08-01 06:17:08.995 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 315/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 9.513e-04, size: 288, ETA: 1:48:19
2025-08-01 06:17:12.359 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 315/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 9.504e-04, size: 544, ETA: 1:48:15
2025-08-01 06:17:15.961 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 315/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.177s, 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: 9.496e-04, size: 480, ETA: 1:48:12
2025-08-01 06:17:19.519 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 315/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.175s, data_time: 0.005s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.9, lr: 9.488e-04, size: 544, ETA: 1:48:08
2025-08-01 06:17:22.951 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 315/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 3.5, cls_loss: 0.7, lr: 9.480e-04, size: 384, ETA: 1:48:05
2025-08-01 06:17:26.136 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 315/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 9.471e-04, size: 320, ETA: 1:48:01
2025-08-01 06:17:27.493 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:17:34.230 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:17:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:17:35.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5083
2025-08-01 06:17:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4805
2025-08-01 06:17:36.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2969
2025-08-01 06:17:36.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4285
2025-08-01 06:17:36.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:17:36.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:17:36.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:17:36.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:17:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:17:38.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:17:38.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:17:39.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:17:40.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:17:41.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:17:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:17:43.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:17:44.323 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:17:44.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 06:17:44.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 06:17:44.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:17:44.332 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 06:17:44.333 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:17:44.418 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:17:44.549 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch316
2025-08-01 06:17:47.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 316/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 9.460e-04, size: 320, ETA: 1:47:55
2025-08-01 06:17:51.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 316/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, 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: 9.451e-04, size: 512, ETA: 1:47:51
2025-08-01 06:17:54.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 316/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 1.1, lr: 9.443e-04, size: 576, ETA: 1:47:48
2025-08-01 06:17:58.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 316/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 9.435e-04, size: 512, ETA: 1:47:44
2025-08-01 06:18:01.251 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 316/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 9.427e-04, size: 352, ETA: 1:47:40
2025-08-01 06:18:04.389 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 316/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 1.0, lr: 9.418e-04, size: 416, ETA: 1:47:36
2025-08-01 06:18:05.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:18:12.656 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:18:13.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:18:13.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4756
2025-08-01 06:18:13.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4545
2025-08-01 06:18:14.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2763
2025-08-01 06:18:14.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4021
2025-08-01 06:18:14.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:18:14.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:18:14.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-01 06:18:14.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 06:18:14.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.402
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:18:14.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:18:14.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:18:15.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:18:15.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:18:16.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:18:16.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:18:17.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:18:17.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:18:18.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:18:18.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:18:18.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:18:18.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 06:18:18.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:18:18.972 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.81 ms, Average inference time: 8.31 ms

2025-08-01 06:18:18.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:18:19.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:18:19.255 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch317
2025-08-01 06:18:22.573 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 317/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 9.406e-04, size: 576, ETA: 1:47:31
2025-08-01 06:18:26.085 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 317/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 8.9, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 0.8, lr: 9.398e-04, size: 320, ETA: 1:47:27
2025-08-01 06:18:29.378 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 317/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 9.390e-04, size: 448, ETA: 1:47:24
2025-08-01 06:18:32.864 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 317/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, 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: 9.382e-04, size: 384, ETA: 1:47:20
2025-08-01 06:18:36.101 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 317/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, 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: 9.373e-04, size: 512, ETA: 1:47:16
2025-08-01 06:18:39.490 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 317/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 9.365e-04, size: 288, ETA: 1:47:12
2025-08-01 06:18:41.129 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:18:47.638 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:18:48.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:18:48.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3315
2025-08-01 06:18:48.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2828
2025-08-01 06:18:48.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1298
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2480
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.283
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.130
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.248
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:18:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:18:48.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:18:48.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:18:48.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:18:48.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:18:48.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:18:48.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:18:48.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:18:49.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:18:49.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:18:49.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:18:50.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:18:50.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:18:50.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:18:51.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:18:51.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:18:51.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 06:18:51.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-08-01 06:18:51.632 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:18:51.640 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.73 ms, Average inference time: 8.12 ms

2025-08-01 06:18:51.641 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:18:51.759 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:18:51.878 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch318
2025-08-01 06:18:55.221 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 318/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.7, lr: 9.353e-04, size: 576, ETA: 1:47:07
2025-08-01 06:18:58.628 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 318/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.6, lr: 9.345e-04, size: 448, ETA: 1:47:03
2025-08-01 06:19:02.156 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 318/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 9.337e-04, size: 320, ETA: 1:47:00
2025-08-01 06:19:05.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 318/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.184s, data_time: 0.004s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.9, lr: 9.329e-04, size: 320, ETA: 1:46:56
2025-08-01 06:19:09.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 318/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 9.320e-04, size: 448, ETA: 1:46:53
2025-08-01 06:19:12.915 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 318/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 9.312e-04, size: 320, ETA: 1:46:49
2025-08-01 06:19:14.459 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:19:21.099 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:19:21.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:19:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5134
2025-08-01 06:19:22.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4538
2025-08-01 06:19:22.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2589
2025-08-01 06:19:22.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4087
2025-08-01 06:19:22.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:19:22.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:19:22.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-01 06:19:22.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 06:19:22.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.259
2025-08-01 06:19:22.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-08-01 06:19:22.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:19:22.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:19:22.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:19:22.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:19:22.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:19:22.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:19:22.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:19:22.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:19:22.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:19:23.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:19:23.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:19:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:19:25.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:19:25.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:19:26.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:19:27.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:19:27.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:19:28.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:19:28.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:19:28.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:19:28.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:19:28.405 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.85 ms, Average inference time: 8.43 ms

2025-08-01 06:19:28.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:19:28.523 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:19:28.605 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch319
2025-08-01 06:19:32.008 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 319/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.9, lr: 9.300e-04, size: 512, ETA: 1:46:44
2025-08-01 06:19:35.477 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 319/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.6, lr: 9.292e-04, size: 576, ETA: 1:46:40
2025-08-01 06:19:38.874 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 319/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 9.284e-04, size: 416, ETA: 1:46:36
2025-08-01 06:19:42.238 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 319/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 4.5, cls_loss: 0.8, lr: 9.275e-04, size: 352, ETA: 1:46:33
2025-08-01 06:19:45.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 319/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 3.5, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 1.2, cls_loss: 0.5, lr: 9.267e-04, size: 416, ETA: 1:46:29
2025-08-01 06:19:48.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 319/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 9.259e-04, size: 384, ETA: 1:46:25
2025-08-01 06:19:50.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:19:56.970 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:19:57.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:19:58.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4230
2025-08-01 06:19:58.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3807
2025-08-01 06:19:58.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2114
2025-08-01 06:19:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3383
2025-08-01 06:19:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:19:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:19:58.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-08-01 06:19:58.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-01 06:19:58.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-01 06:19:58.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.338
2025-08-01 06:19:58.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:19:58.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:19:58.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:19:58.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:19:58.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:19:58.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:19:58.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:19:58.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:19:58.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:19:58.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:19:59.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:20:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:20:00.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:20:01.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:20:01.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:20:02.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:20:02.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:20:03.514 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:20:03.514 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:20:03.514 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 06:20:03.514 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:20:03.522 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.86 ms, Average inference time: 8.34 ms

2025-08-01 06:20:03.523 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:20:03.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:20:03.679 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch320
2025-08-01 06:20:06.882 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 320/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 1.3, cls_loss: 0.8, lr: 9.247e-04, size: 416, ETA: 1:46:19
2025-08-01 06:20:10.194 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 320/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 9.239e-04, size: 576, ETA: 1:46:16
2025-08-01 06:20:13.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 320/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.6, lr: 9.231e-04, size: 256, ETA: 1:46:12
2025-08-01 06:20:16.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 320/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.160s, 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: 9.222e-04, size: 416, ETA: 1:46:08
2025-08-01 06:20:20.377 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 320/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 9.0, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.8, lr: 9.214e-04, size: 256, ETA: 1:46:05
2025-08-01 06:20:23.675 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 320/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.159s, 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: 9.206e-04, size: 256, ETA: 1:46:01
2025-08-01 06:20:25.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:20:31.904 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:20:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:20:33.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4797
2025-08-01 06:20:33.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3730
2025-08-01 06:20:33.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2698
2025-08-01 06:20:33.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3742
2025-08-01 06:20:33.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:20:33.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:20:33.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-08-01 06:20:33.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-01 06:20:33.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-08-01 06:20:33.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.374
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:20:33.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:20:34.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:20:35.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:20:36.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:20:37.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:20:38.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:20:39.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:20:39.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:20:40.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:20:41.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:20:41.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:20:41.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 06:20:41.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:20:41.733 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.84 ms, Average inference time: 8.22 ms

2025-08-01 06:20:41.734 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:20:41.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:20:41.993 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch321
2025-08-01 06:20:45.097 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 321/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 9.194e-04, size: 416, ETA: 1:45:55
2025-08-01 06:20:48.575 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 321/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 9.186e-04, size: 448, ETA: 1:45:52
2025-08-01 06:20:51.946 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 321/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 9.177e-04, size: 416, ETA: 1:45:48
2025-08-01 06:20:55.374 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 321/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, 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: 9.169e-04, size: 544, ETA: 1:45:44
2025-08-01 06:20:58.536 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 321/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 9.161e-04, size: 288, ETA: 1:45:40
2025-08-01 06:21:01.813 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 321/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 9.153e-04, size: 416, ETA: 1:45:37
2025-08-01 06:21:03.271 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:21:09.950 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:21:10.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:21:10.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4851
2025-08-01 06:21:10.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4341
2025-08-01 06:21:11.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3181
2025-08-01 06:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4124
2025-08-01 06:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:21:11.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:21:11.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:21:11.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:21:11.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:21:12.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:21:12.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:21:13.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:21:13.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:21:14.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:21:14.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:21:15.160 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:21:15.160 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:21:15.160 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:21:15.160 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:21:15.167 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.83 ms, Average inference time: 8.23 ms

2025-08-01 06:21:15.168 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:21:15.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:21:15.333 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch322
2025-08-01 06:21:18.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 322/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.7, lr: 9.141e-04, size: 384, ETA: 1:45:31
2025-08-01 06:21:21.924 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 322/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 9.133e-04, size: 480, ETA: 1:45:27
2025-08-01 06:21:25.420 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 322/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.9, lr: 9.124e-04, size: 352, ETA: 1:45:24
2025-08-01 06:21:28.850 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 322/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.8, lr: 9.116e-04, size: 576, ETA: 1:45:20
2025-08-01 06:21:32.398 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 322/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 9.108e-04, size: 576, ETA: 1:45:16
2025-08-01 06:21:35.776 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 322/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 9.100e-04, size: 512, ETA: 1:45:13
2025-08-01 06:21:37.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:21:43.925 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:21:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:21:45.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4997
2025-08-01 06:21:45.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4556
2025-08-01 06:21:45.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2631
2025-08-01 06:21:45.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4061
2025-08-01 06:21:45.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:21:45.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:21:45.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-01 06:21:45.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-01 06:21:45.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-08-01 06:21:45.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.406
2025-08-01 06:21:45.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:21:45.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:21:45.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:21:45.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:21:45.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:21:45.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:21:45.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:21:45.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:21:45.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:21:45.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:21:46.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:21:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:21:47.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:21:48.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:21:48.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:21:49.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:21:50.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:21:50.686 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:21:50.686 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:21:50.686 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:21:50.686 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:21:50.694 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.80 ms, Average inference time: 8.19 ms

2025-08-01 06:21:50.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:21:50.782 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:21:50.861 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch323
2025-08-01 06:21:53.916 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 323/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.5, lr: 9.088e-04, size: 288, ETA: 1:45:07
2025-08-01 06:21:57.223 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 323/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.8, lr: 9.080e-04, size: 416, ETA: 1:45:03
2025-08-01 06:22:00.613 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 323/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 9.071e-04, size: 512, ETA: 1:45:00
2025-08-01 06:22:04.049 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 323/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 9.063e-04, size: 320, ETA: 1:44:56
2025-08-01 06:22:07.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 323/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.9, lr: 9.055e-04, size: 576, ETA: 1:44:52
2025-08-01 06:22:11.026 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 323/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 9.047e-04, size: 480, ETA: 1:44:49
2025-08-01 06:22:12.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:22:19.290 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:22:20.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:22:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5331
2025-08-01 06:22:20.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4719
2025-08-01 06:22:20.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2666
2025-08-01 06:22:20.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4239
2025-08-01 06:22:20.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.424
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:22:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:22:20.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:22:20.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:22:21.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:22:21.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:22:22.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:22:22.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:22:23.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:22:23.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:22:23.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:22:24.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:22:24.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:22:24.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:22:24.960 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 06:22:24.960 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:22:24.967 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.81 ms, Average inference time: 8.18 ms

2025-08-01 06:22:24.968 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:22:25.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:22:25.150 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch324
2025-08-01 06:22:28.424 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 324/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 9.035e-04, size: 480, ETA: 1:44:43
2025-08-01 06:22:31.855 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 324/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 9.027e-04, size: 352, ETA: 1:44:40
2025-08-01 06:22:35.265 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 324/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.6, lr: 9.018e-04, size: 576, ETA: 1:44:36
2025-08-01 06:22:38.842 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 324/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 1.1, lr: 9.010e-04, size: 416, ETA: 1:44:33
2025-08-01 06:22:42.178 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 324/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 1.6, cls_loss: 0.6, lr: 9.002e-04, size: 544, ETA: 1:44:29
2025-08-01 06:22:45.573 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 324/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 8.994e-04, size: 384, ETA: 1:44:25
2025-08-01 06:22:46.986 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:22:53.702 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:22:54.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:22:54.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4381
2025-08-01 06:22:54.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3483
2025-08-01 06:22:54.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2278
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3381
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.228
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.338
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:22:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:22:54.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:22:54.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:22:54.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:22:54.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:22:54.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:22:54.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:22:54.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:22:55.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:22:55.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:22:56.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:22:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:22:57.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:22:57.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:22:58.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:22:58.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:22:59.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:22:59.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:22:59.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 06:22:59.077 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:22:59.088 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.80 ms, Average inference time: 8.24 ms

2025-08-01 06:22:59.090 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:22:59.196 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:22:59.275 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch325
2025-08-01 06:23:02.500 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 325/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 8.982e-04, size: 448, ETA: 1:44:19
2025-08-01 06:23:05.807 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 325/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 8.974e-04, size: 448, ETA: 1:44:16
2025-08-01 06:23:09.289 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 325/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 8.965e-04, size: 416, ETA: 1:44:12
2025-08-01 06:23:12.507 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 325/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 8.957e-04, size: 352, ETA: 1:44:08
2025-08-01 06:23:16.013 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 325/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 8.949e-04, size: 320, ETA: 1:44:05
2025-08-01 06:23:19.459 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 325/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 8.941e-04, size: 352, ETA: 1:44:01
2025-08-01 06:23:21.098 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:23:27.928 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:23:28.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:23:28.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4239
2025-08-01 06:23:28.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3924
2025-08-01 06:23:29.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1943
2025-08-01 06:23:29.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3368
2025-08-01 06:23:29.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:23:29.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:23:29.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 06:23:29.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 06:23:29.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.194
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:23:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:23:29.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:23:29.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:23:29.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:23:30.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:23:30.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:23:31.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:23:31.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:23:32.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:23:32.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:23:33.137 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:23:33.138 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:23:33.138 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 06:23:33.139 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:23:33.146 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 06:23:33.147 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:23:33.231 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:23:33.310 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch326
2025-08-01 06:23:36.875 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 326/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 8.929e-04, size: 384, ETA: 1:43:56
2025-08-01 06:23:40.151 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 326/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.8, lr: 8.921e-04, size: 320, ETA: 1:43:52
2025-08-01 06:23:43.490 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 326/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, 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: 8.912e-04, size: 288, ETA: 1:43:48
2025-08-01 06:23:46.944 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 326/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.7, lr: 8.904e-04, size: 256, ETA: 1:43:45
2025-08-01 06:23:50.414 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 326/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 8.896e-04, size: 576, ETA: 1:43:41
2025-08-01 06:23:53.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 326/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 8.888e-04, size: 448, ETA: 1:43:37
2025-08-01 06:23:55.422 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:24:02.074 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:24:02.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:24:03.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5257
2025-08-01 06:24:03.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4702
2025-08-01 06:24:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2925
2025-08-01 06:24:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4295
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:24:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:24:03.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:24:03.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:24:03.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:24:03.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:24:03.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:24:03.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:24:04.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:24:04.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:24:05.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:24:06.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:24:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:24:07.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:24:08.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:24:08.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:24:09.491 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:24:09.491 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-01 06:24:09.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 06:24:09.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:24:09.499 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.83 ms, Average inference time: 8.26 ms

2025-08-01 06:24:09.500 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:24:09.576 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:24:09.660 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch327
2025-08-01 06:24:12.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 327/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 8.876e-04, size: 544, ETA: 1:43:32
2025-08-01 06:24:16.182 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 327/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 8.868e-04, size: 480, ETA: 1:43:28
2025-08-01 06:24:19.507 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 327/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, 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: 8.860e-04, size: 288, ETA: 1:43:24
2025-08-01 06:24:22.718 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 327/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, 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: 8.851e-04, size: 448, ETA: 1:43:21
2025-08-01 06:24:26.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 327/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 8.843e-04, size: 544, ETA: 1:43:17
2025-08-01 06:24:29.512 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 327/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 8.835e-04, size: 320, ETA: 1:43:13
2025-08-01 06:24:30.925 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:24:37.541 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:24:38.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:24:39.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5006
2025-08-01 06:24:39.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4592
2025-08-01 06:24:39.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2750
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4116
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:24:39.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:24:39.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:24:39.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:24:39.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:24:39.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:24:39.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:24:39.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:24:40.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:24:41.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:24:42.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:24:43.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:24:44.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:24:45.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:24:46.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:24:47.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:24:48.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:24:48.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:24:48.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:24:48.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:24:48.635 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.86 ms, Average inference time: 8.28 ms

2025-08-01 06:24:48.636 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:24:48.757 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:24:48.843 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch328
2025-08-01 06:24:52.068 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 328/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 8.823e-04, size: 544, ETA: 1:43:08
2025-08-01 06:24:55.529 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 328/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 8.815e-04, size: 544, ETA: 1:43:04
2025-08-01 06:24:59.192 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 328/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.9, lr: 8.807e-04, size: 416, ETA: 1:43:01
2025-08-01 06:25:02.446 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 328/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 8.798e-04, size: 544, ETA: 1:42:57
2025-08-01 06:25:05.846 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 328/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 8.790e-04, size: 544, ETA: 1:42:53
2025-08-01 06:25:09.177 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 328/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 0.9, lr: 8.782e-04, size: 288, ETA: 1:42:50
2025-08-01 06:25:10.587 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:25:17.275 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:25:18.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:25:18.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4998
2025-08-01 06:25:18.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4138
2025-08-01 06:25:19.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3128
2025-08-01 06:25:19.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4088
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:25:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:25:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:25:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:25:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:25:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:25:19.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:25:20.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:25:21.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:25:22.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:25:22.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:25:23.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:25:24.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:25:25.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:25:26.003 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:25:26.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:25:26.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:25:26.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:25:26.011 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.85 ms, Average inference time: 8.37 ms

2025-08-01 06:25:26.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:25:26.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:25:26.181 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch329
2025-08-01 06:25:29.535 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 329/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.9, lr: 8.770e-04, size: 288, ETA: 1:42:44
2025-08-01 06:25:32.810 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 329/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.9, lr: 8.762e-04, size: 480, ETA: 1:42:40
2025-08-01 06:25:36.380 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 329/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.8, lr: 8.754e-04, size: 576, ETA: 1:42:37
2025-08-01 06:25:39.764 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 329/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 8.746e-04, size: 352, ETA: 1:42:33
2025-08-01 06:25:42.950 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 329/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.9, lr: 8.737e-04, size: 288, ETA: 1:42:29
2025-08-01 06:25:46.296 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 329/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 8.729e-04, size: 256, ETA: 1:42:25
2025-08-01 06:25:47.704 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:25:54.364 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:25:55.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:25:56.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4796
2025-08-01 06:25:56.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3591
2025-08-01 06:25:56.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2469
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3619
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.247
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.362
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:25:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:25:56.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:25:56.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:25:56.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:25:56.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:25:56.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:25:56.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:25:58.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:25:59.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:26:00.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:26:01.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:26:02.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:26:04.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:26:05.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:26:06.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:26:07.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:26:07.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:26:07.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:26:07.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:26:07.626 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.87 ms, Average inference time: 8.26 ms

2025-08-01 06:26:07.627 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:26:07.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:26:07.790 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch330
2025-08-01 06:26:11.054 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 330/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.8, lr: 8.717e-04, size: 256, ETA: 1:42:20
2025-08-01 06:26:14.222 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 330/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 8.8, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.8, lr: 8.709e-04, size: 416, ETA: 1:42:16
2025-08-01 06:26:17.578 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 330/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 8.701e-04, size: 384, ETA: 1:42:12
2025-08-01 06:26:20.894 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 330/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 8.693e-04, size: 544, ETA: 1:42:09
2025-08-01 06:26:24.270 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 330/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.8, lr: 8.685e-04, size: 256, ETA: 1:42:05
2025-08-01 06:26:27.750 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 330/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 8.676e-04, size: 256, ETA: 1:42:01
2025-08-01 06:26:29.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:26:35.907 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:26:37.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:26:38.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4003
2025-08-01 06:26:38.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3942
2025-08-01 06:26:38.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2061
2025-08-01 06:26:38.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3336
2025-08-01 06:26:38.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:26:38.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:26:38.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 06:26:38.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-01 06:26:38.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.206
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.334
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:26:38.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:26:39.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:26:40.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:26:41.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:26:42.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:26:43.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:26:44.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:26:45.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:26:46.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:26:48.059 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:26:48.059 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:26:48.059 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 06:26:48.059 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:26:48.067 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.88 ms, Average inference time: 8.25 ms

2025-08-01 06:26:48.069 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:26:48.153 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:26:48.237 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch331
2025-08-01 06:26:51.482 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 331/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, 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: 8.665e-04, size: 448, ETA: 1:41:56
2025-08-01 06:26:54.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 331/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 8.656e-04, size: 416, ETA: 1:41:52
2025-08-01 06:26:58.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 331/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.8, lr: 8.648e-04, size: 288, ETA: 1:41:48
2025-08-01 06:27:01.381 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 331/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.6, lr: 8.640e-04, size: 352, ETA: 1:41:44
2025-08-01 06:27:04.730 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 331/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 8.632e-04, size: 384, ETA: 1:41:41
2025-08-01 06:27:07.955 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 331/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 8.624e-04, size: 384, ETA: 1:41:37
2025-08-01 06:27:09.359 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:27:16.171 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:27:17.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:27:18.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4915
2025-08-01 06:27:18.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4554
2025-08-01 06:27:18.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2882
2025-08-01 06:27:18.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4117
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:27:18.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:27:18.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:27:18.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:27:18.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:27:18.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:27:18.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:27:18.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:27:20.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:27:21.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:27:22.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:27:24.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:27:25.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:27:26.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:27:28.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:27:29.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:27:30.653 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:27:30.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:27:30.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:27:30.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:27:30.662 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.89 ms, Average inference time: 8.41 ms

2025-08-01 06:27:30.663 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:27:30.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:27:30.824 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch332
2025-08-01 06:27:34.234 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 332/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 8.612e-04, size: 512, ETA: 1:41:32
2025-08-01 06:27:37.683 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 332/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 8.604e-04, size: 384, ETA: 1:41:28
2025-08-01 06:27:41.051 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 332/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 8.595e-04, size: 384, ETA: 1:41:24
2025-08-01 06:27:44.235 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 332/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 8.587e-04, size: 384, ETA: 1:41:20
2025-08-01 06:27:47.446 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 332/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.8, lr: 8.579e-04, size: 352, ETA: 1:41:17
2025-08-01 06:27:50.662 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 332/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 8.571e-04, size: 416, ETA: 1:41:13
2025-08-01 06:27:52.135 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:27:58.795 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:27:59.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:27:59.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3340
2025-08-01 06:27:59.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2350
2025-08-01 06:27:59.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1520
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2404
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.152
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.240
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:27:59.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:27:59.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:27:59.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:27:59.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:27:59.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:27:59.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:27:59.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:27:59.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:27:59.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:27:59.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:28:00.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:28:00.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:28:00.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:28:01.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:28:01.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:28:01.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:28:01.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:28:01.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 06:28:01.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 06:28:01.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:28:01.823 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.83 ms, Average inference time: 8.24 ms

2025-08-01 06:28:01.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:28:01.937 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:28:02.016 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch333
2025-08-01 06:28:05.304 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 333/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 8.559e-04, size: 576, ETA: 1:41:07
2025-08-01 06:28:08.874 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 333/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 8.551e-04, size: 448, ETA: 1:41:04
2025-08-01 06:28:12.221 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 333/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.8, lr: 8.543e-04, size: 256, ETA: 1:41:00
2025-08-01 06:28:16.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 333/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.186s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 8.535e-04, size: 512, ETA: 1:40:57
2025-08-01 06:28:19.365 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 333/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 8.526e-04, size: 448, ETA: 1:40:53
2025-08-01 06:28:22.735 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 333/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 9.6, iou_loss: 3.0, l1_loss: 1.7, conf_loss: 4.2, cls_loss: 0.7, lr: 8.518e-04, size: 576, ETA: 1:40:49
2025-08-01 06:28:24.309 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:28:31.101 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:28:32.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:28:33.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4541
2025-08-01 06:28:33.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4174
2025-08-01 06:28:33.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2288
2025-08-01 06:28:33.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3667
2025-08-01 06:28:33.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:28:33.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:28:33.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.229
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.367
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:28:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:28:33.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:28:34.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:28:35.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:28:36.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:28:37.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:28:38.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:28:39.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:28:40.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:28:41.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:28:42.252 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:28:42.252 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:28:42.252 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 06:28:42.252 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:28:42.260 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.86 ms, Average inference time: 8.39 ms

2025-08-01 06:28:42.268 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:28:42.349 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:28:42.432 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch334
2025-08-01 06:28:45.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 334/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 1.1, lr: 8.506e-04, size: 256, ETA: 1:40:44
2025-08-01 06:28:49.003 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 334/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 8.498e-04, size: 416, ETA: 1:40:40
2025-08-01 06:28:52.425 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 334/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, 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: 8.490e-04, size: 448, ETA: 1:40:37
2025-08-01 06:28:55.720 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 334/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 8.482e-04, size: 480, ETA: 1:40:33
2025-08-01 06:28:59.292 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 334/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.1, iou_loss: 1.8, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.6, lr: 8.474e-04, size: 544, ETA: 1:40:29
2025-08-01 06:29:02.796 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 334/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 9.2, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 4.3, cls_loss: 1.0, lr: 8.466e-04, size: 512, ETA: 1:40:26
2025-08-01 06:29:04.298 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:29:11.163 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:29:12.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:29:13.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4582
2025-08-01 06:29:13.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4038
2025-08-01 06:29:13.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2165
2025-08-01 06:29:13.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3595
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:29:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:29:13.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:29:13.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:29:13.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:29:13.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:29:13.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:29:14.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:29:15.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:29:16.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:29:17.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:29:18.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:29:19.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:29:20.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:29:21.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:29:22.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:29:22.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:29:22.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:29:22.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:29:22.110 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.82 ms, Average inference time: 8.15 ms

2025-08-01 06:29:22.111 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:29:22.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:29:22.278 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch335
2025-08-01 06:29:25.384 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 335/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 8.454e-04, size: 512, ETA: 1:40:20
2025-08-01 06:29:28.747 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 335/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 8.446e-04, size: 352, ETA: 1:40:17
2025-08-01 06:29:32.198 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 335/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 8.437e-04, size: 544, ETA: 1:40:13
2025-08-01 06:29:35.577 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 335/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.4, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 8.429e-04, size: 288, ETA: 1:40:09
2025-08-01 06:29:39.081 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 335/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 8.421e-04, size: 576, ETA: 1:40:06
2025-08-01 06:29:42.508 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 335/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.7, lr: 8.413e-04, size: 288, ETA: 1:40:02
2025-08-01 06:29:43.998 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:29:50.933 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:29:51.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:29:51.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4089
2025-08-01 06:29:51.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3915
2025-08-01 06:29:51.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1890
2025-08-01 06:29:51.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3298
2025-08-01 06:29:51.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:29:51.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.189
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.330
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:29:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:29:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:29:52.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:29:52.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:29:53.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:29:53.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:29:53.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:29:54.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:29:54.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:29:55.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:29:55.684 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:29:55.684 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:29:55.684 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 06:29:55.684 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:29:55.694 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.79 ms, Average inference time: 8.21 ms

2025-08-01 06:29:55.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:29:55.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:29:55.916 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch336
2025-08-01 06:29:59.052 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 336/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 8.401e-04, size: 576, ETA: 1:39:57
2025-08-01 06:30:02.531 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 336/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 8.393e-04, size: 576, ETA: 1:39:53
2025-08-01 06:30:06.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 336/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.9, lr: 8.385e-04, size: 320, ETA: 1:39:49
2025-08-01 06:30:09.417 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 336/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.8, lr: 8.377e-04, size: 512, ETA: 1:39:46
2025-08-01 06:30:12.867 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 336/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 8.369e-04, size: 416, ETA: 1:39:42
2025-08-01 06:30:16.215 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 336/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.9, lr: 8.360e-04, size: 448, ETA: 1:39:38
2025-08-01 06:30:17.588 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:30:24.427 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:30:25.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:30:25.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3595
2025-08-01 06:30:25.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3209
2025-08-01 06:30:25.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1717
2025-08-01 06:30:25.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2840
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.172
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.284
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:30:25.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:30:25.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:30:25.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:30:25.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:30:25.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:30:25.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:30:25.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:30:26.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:30:27.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:30:27.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:30:28.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:30:28.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:30:29.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:30:30.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:30:30.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:30:31.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:30:31.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 06:30:31.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 06:30:31.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:30:31.414 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.89 ms, Average inference time: 8.44 ms

2025-08-01 06:30:31.415 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:30:31.500 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:30:31.579 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch337
2025-08-01 06:30:35.067 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 337/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.6, lr: 8.349e-04, size: 512, ETA: 1:39:33
2025-08-01 06:30:38.584 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 337/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 8.341e-04, size: 480, ETA: 1:39:30
2025-08-01 06:30:41.812 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 337/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 8.332e-04, size: 448, ETA: 1:39:26
2025-08-01 06:30:45.051 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 337/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.5, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 8.324e-04, size: 544, ETA: 1:39:22
2025-08-01 06:30:48.483 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 337/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 1.1, lr: 8.316e-04, size: 448, ETA: 1:39:18
2025-08-01 06:30:51.966 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 337/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 8.308e-04, size: 544, ETA: 1:39:15
2025-08-01 06:30:53.518 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:31:00.165 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:31:00.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:31:01.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4707
2025-08-01 06:31:01.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3902
2025-08-01 06:31:01.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2317
2025-08-01 06:31:01.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3642
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.364
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:31:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:31:01.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:31:01.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:31:01.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:31:01.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:31:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:31:02.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:31:03.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:31:03.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:31:04.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:31:04.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:31:04.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:31:05.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:31:05.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:31:05.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:31:05.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:31:05.314 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.81 ms, Average inference time: 8.36 ms

2025-08-01 06:31:05.316 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:31:05.430 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:31:05.511 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch338
2025-08-01 06:31:08.882 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 338/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 2.4, cls_loss: 0.6, lr: 8.296e-04, size: 320, ETA: 1:39:10
2025-08-01 06:31:12.063 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 338/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 8.288e-04, size: 544, ETA: 1:39:06
2025-08-01 06:31:15.525 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 338/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 8.280e-04, size: 256, ETA: 1:39:02
2025-08-01 06:31:18.743 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 338/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 8.272e-04, size: 544, ETA: 1:38:58
2025-08-01 06:31:22.103 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 338/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 8.264e-04, size: 384, ETA: 1:38:55
2025-08-01 06:31:25.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 338/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 1.0, lr: 8.256e-04, size: 288, ETA: 1:38:51
2025-08-01 06:31:26.878 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:31:33.597 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:31:34.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:31:34.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4248
2025-08-01 06:31:35.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3106
2025-08-01 06:31:35.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1953
2025-08-01 06:31:35.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3102
2025-08-01 06:31:35.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:31:35.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:31:35.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-01 06:31:35.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-01 06:31:35.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.310
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:31:35.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:31:35.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:31:36.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:31:37.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:31:37.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:31:38.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:31:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:31:39.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:31:40.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:31:41.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:31:41.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 06:31:41.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 06:31:41.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:31:41.322 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.87 ms, Average inference time: 8.32 ms

2025-08-01 06:31:41.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:31:41.409 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:31:41.533 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch339
2025-08-01 06:31:44.737 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 339/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.7, lr: 8.244e-04, size: 480, ETA: 1:38:46
2025-08-01 06:31:48.034 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 339/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 8.236e-04, size: 576, ETA: 1:38:42
2025-08-01 06:31:51.562 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 339/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 8.227e-04, size: 256, ETA: 1:38:38
2025-08-01 06:31:54.808 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 339/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 8.219e-04, size: 288, ETA: 1:38:34
2025-08-01 06:31:58.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 339/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 8.211e-04, size: 576, ETA: 1:38:31
2025-08-01 06:32:01.531 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 339/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.7, lr: 8.203e-04, size: 384, ETA: 1:38:27
2025-08-01 06:32:03.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:32:09.894 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:32:10.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:32:11.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4753
2025-08-01 06:32:11.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4529
2025-08-01 06:32:11.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2243
2025-08-01 06:32:11.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3842
2025-08-01 06:32:11.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:32:11.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:32:11.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-01 06:32:11.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.384
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:32:11.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:32:11.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:32:12.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:32:13.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:32:14.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:32:15.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:32:15.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:32:16.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:32:17.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:32:18.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:32:18.981 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:32:18.981 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:32:18.981 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 06:32:18.982 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:32:18.989 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.88 ms, Average inference time: 8.38 ms

2025-08-01 06:32:18.991 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:32:19.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:32:19.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch340
2025-08-01 06:32:22.422 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 340/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 8.191e-04, size: 448, ETA: 1:38:22
2025-08-01 06:32:25.875 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 340/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.8, lr: 8.183e-04, size: 576, ETA: 1:38:18
2025-08-01 06:32:29.328 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 340/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.9, lr: 8.175e-04, size: 512, ETA: 1:38:15
2025-08-01 06:32:32.763 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 340/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 8.167e-04, size: 416, ETA: 1:38:11
2025-08-01 06:32:36.129 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 340/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 4.0, cls_loss: 0.8, lr: 8.159e-04, size: 448, ETA: 1:38:07
2025-08-01 06:32:39.338 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 340/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 8.151e-04, size: 448, ETA: 1:38:03
2025-08-01 06:32:40.831 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:32:47.552 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:32:48.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:32:49.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4727
2025-08-01 06:32:49.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4333
2025-08-01 06:32:49.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2805
2025-08-01 06:32:49.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3955
2025-08-01 06:32:49.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:32:49.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:32:49.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:32:49.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:32:49.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:32:49.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:32:50.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:32:51.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:32:51.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:32:52.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:32:53.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:32:54.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:32:55.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:32:56.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:32:57.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:32:57.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:32:57.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 06:32:57.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:32:57.347 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.87 ms, Average inference time: 8.28 ms

2025-08-01 06:32:57.349 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:32:57.440 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:32:57.534 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch341
2025-08-01 06:33:00.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 341/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 8.139e-04, size: 448, ETA: 1:37:58
2025-08-01 06:33:04.204 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 341/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 4.5, cls_loss: 0.6, lr: 8.131e-04, size: 576, ETA: 1:37:54
2025-08-01 06:33:07.667 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 341/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, 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: 8.123e-04, size: 448, ETA: 1:37:51
2025-08-01 06:33:10.891 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 341/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 8.115e-04, size: 384, ETA: 1:37:47
2025-08-01 06:33:14.097 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 341/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 8.107e-04, size: 416, ETA: 1:37:43
2025-08-01 06:33:17.453 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 341/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 1.1, lr: 8.098e-04, size: 512, ETA: 1:37:40
2025-08-01 06:33:19.035 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:33:25.697 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:33:26.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:33:26.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4521
2025-08-01 06:33:26.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4182
2025-08-01 06:33:26.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2113
2025-08-01 06:33:26.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3606
2025-08-01 06:33:26.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:33:26.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:33:26.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 06:33:26.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:33:26.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:33:27.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:33:27.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:33:28.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:33:28.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:33:29.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:33:29.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:33:29.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:33:30.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:33:30.936 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:33:30.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:33:30.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:33:30.938 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:33:30.951 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.59 ms, Average NMS time: 0.83 ms, Average inference time: 8.42 ms

2025-08-01 06:33:30.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:33:31.065 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:33:31.173 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch342
2025-08-01 06:33:34.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 342/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.8, lr: 8.087e-04, size: 320, ETA: 1:37:34
2025-08-01 06:33:37.875 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 342/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 8.079e-04, size: 544, ETA: 1:37:31
2025-08-01 06:33:41.210 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 342/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.005s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 8.071e-04, size: 416, ETA: 1:37:27
2025-08-01 06:33:44.681 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 342/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 8.062e-04, size: 512, ETA: 1:37:23
2025-08-01 06:33:48.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 342/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 8.054e-04, size: 448, ETA: 1:37:20
2025-08-01 06:33:51.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 342/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 9.1, iou_loss: 2.1, l1_loss: 1.4, conf_loss: 4.2, cls_loss: 1.4, lr: 8.046e-04, size: 576, ETA: 1:37:16
2025-08-01 06:33:53.263 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:33:59.938 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:34:00.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:34:01.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4862
2025-08-01 06:34:01.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4630
2025-08-01 06:34:01.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2838
2025-08-01 06:34:01.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4110
2025-08-01 06:34:01.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:34:01.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:34:01.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-01 06:34:01.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-01 06:34:01.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.284
2025-08-01 06:34:01.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.411
2025-08-01 06:34:01.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:34:01.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:34:01.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:34:01.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:34:01.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:34:01.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:34:01.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:34:01.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:34:01.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:34:02.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:34:02.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:34:03.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:34:03.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:34:04.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:34:05.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:34:05.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:34:06.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:34:06.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:34:06.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 06:34:06.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:34:06.978 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:34:06.984 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.86 ms, Average inference time: 8.28 ms

2025-08-01 06:34:06.986 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:34:07.065 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:34:07.154 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch343
2025-08-01 06:34:10.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 343/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 8.034e-04, size: 320, ETA: 1:37:11
2025-08-01 06:34:14.059 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 343/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.185s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.6, lr: 8.026e-04, size: 416, ETA: 1:37:08
2025-08-01 06:34:17.560 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 343/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 8.018e-04, size: 320, ETA: 1:37:04
2025-08-01 06:34:20.929 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 343/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.9, lr: 8.010e-04, size: 416, ETA: 1:37:00
2025-08-01 06:34:24.188 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 343/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.5, lr: 8.002e-04, size: 320, ETA: 1:36:57
2025-08-01 06:34:27.535 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 343/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 0.7, lr: 7.994e-04, size: 480, ETA: 1:36:53
2025-08-01 06:34:29.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:34:35.899 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:34:37.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:34:37.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3517
2025-08-01 06:34:37.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3241
2025-08-01 06:34:37.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2122
2025-08-01 06:34:37.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2960
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.212
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.296
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:34:37.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:34:37.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:34:37.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:34:37.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:34:37.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:34:38.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:34:39.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:34:40.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:34:41.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:34:42.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:34:43.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:34:43.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:34:44.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:34:45.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:34:45.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 06:34:45.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 06:34:45.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:34:45.598 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.89 ms, Average inference time: 8.32 ms

2025-08-01 06:34:45.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:34:45.681 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:34:45.764 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch344
2025-08-01 06:34:49.316 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 344/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, 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: 7.982e-04, size: 384, ETA: 1:36:48
2025-08-01 06:34:52.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 344/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.9, lr: 7.974e-04, size: 256, ETA: 1:36:44
2025-08-01 06:34:56.260 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 344/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 7.966e-04, size: 256, ETA: 1:36:41
2025-08-01 06:34:59.587 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 344/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 7.958e-04, size: 576, ETA: 1:36:37
2025-08-01 06:35:03.257 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 344/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.7, lr: 7.950e-04, size: 384, ETA: 1:36:34
2025-08-01 06:35:06.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 344/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 7.942e-04, size: 320, ETA: 1:36:30
2025-08-01 06:35:07.963 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:35:14.937 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:35:16.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:35:16.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3344
2025-08-01 06:35:16.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2963
2025-08-01 06:35:16.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1315
2025-08-01 06:35:16.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2541
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.131
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.254
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:35:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:35:16.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:35:16.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:35:16.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:35:16.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:35:17.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:35:18.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:35:19.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:35:20.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:35:21.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:35:22.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:35:22.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:35:23.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:35:24.670 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:35:24.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 06:35:24.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-08-01 06:35:24.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:35:24.679 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.90 ms, Average inference time: 8.44 ms

2025-08-01 06:35:24.681 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:35:24.814 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:35:24.898 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch345
2025-08-01 06:35:28.146 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 345/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.7, lr: 7.930e-04, size: 576, ETA: 1:36:24
2025-08-01 06:35:31.495 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 345/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 7.922e-04, size: 512, ETA: 1:36:21
2025-08-01 06:35:34.792 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 345/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 1.1, lr: 7.914e-04, size: 416, ETA: 1:36:17
2025-08-01 06:35:38.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 345/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 7.906e-04, size: 352, ETA: 1:36:13
2025-08-01 06:35:41.988 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 345/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 7.898e-04, size: 352, ETA: 1:36:10
2025-08-01 06:35:45.700 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 345/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 7.890e-04, size: 288, ETA: 1:36:06
2025-08-01 06:35:47.184 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:35:53.931 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:35:55.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:35:55.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4834
2025-08-01 06:35:55.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4408
2025-08-01 06:35:55.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2399
2025-08-01 06:35:55.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3880
2025-08-01 06:35:55.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:35:55.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:35:55.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-08-01 06:35:55.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.388
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:35:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:35:55.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:35:55.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:35:56.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:35:57.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:35:58.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:35:59.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:36:00.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:36:01.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:36:02.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:36:03.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:36:04.129 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:36:04.129 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:36:04.129 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 06:36:04.129 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:36:04.138 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.88 ms, Average inference time: 8.23 ms

2025-08-01 06:36:04.139 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:36:04.279 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:36:04.365 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch346
2025-08-01 06:36:07.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 346/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 7.878e-04, size: 544, ETA: 1:36:01
2025-08-01 06:36:11.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 346/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 7.870e-04, size: 576, ETA: 1:35:57
2025-08-01 06:36:14.432 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 346/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.9, lr: 7.862e-04, size: 320, ETA: 1:35:54
2025-08-01 06:36:17.800 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 346/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 7.854e-04, size: 576, ETA: 1:35:50
2025-08-01 06:36:21.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 346/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.5, lr: 7.846e-04, size: 352, ETA: 1:35:47
2025-08-01 06:36:24.474 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 346/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 7.838e-04, size: 288, ETA: 1:35:43
2025-08-01 06:36:25.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:36:32.558 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:36:33.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:36:33.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4312
2025-08-01 06:36:33.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3385
2025-08-01 06:36:33.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2395
2025-08-01 06:36:33.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3364
2025-08-01 06:36:33.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:36:33.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:36:33.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 06:36:33.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-08-01 06:36:33.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-08-01 06:36:33.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:36:33.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:36:34.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:36:35.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:36:35.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:36:36.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:36:36.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:36:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:36:38.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:36:38.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:36:39.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:36:39.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:36:39.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 06:36:39.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:36:39.241 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.82 ms, Average inference time: 8.17 ms

2025-08-01 06:36:39.242 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:36:39.321 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:36:39.401 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch347
2025-08-01 06:36:42.795 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 347/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 7.826e-04, size: 544, ETA: 1:35:37
2025-08-01 06:36:46.146 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 347/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 7.818e-04, size: 352, ETA: 1:35:34
2025-08-01 06:36:49.476 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 347/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 7.810e-04, size: 256, ETA: 1:35:30
2025-08-01 06:36:52.682 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 347/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.5, lr: 7.802e-04, size: 512, ETA: 1:35:26
2025-08-01 06:36:56.035 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 347/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 1.2, lr: 7.794e-04, size: 288, ETA: 1:35:23
2025-08-01 06:36:59.263 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 347/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 7.786e-04, size: 480, ETA: 1:35:19
2025-08-01 06:37:00.749 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:37:07.537 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:37:08.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:37:08.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4267
2025-08-01 06:37:08.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3513
2025-08-01 06:37:08.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2108
2025-08-01 06:37:08.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3296
2025-08-01 06:37:08.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:37:08.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.330
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:37:08.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:37:08.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:37:08.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:37:08.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:37:08.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:37:09.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:37:10.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:37:10.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:37:11.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:37:11.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:37:12.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:37:13.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:37:13.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:37:14.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:37:14.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:37:14.401 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 06:37:14.401 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:37:14.408 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.86 ms, Average inference time: 8.26 ms

2025-08-01 06:37:14.409 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:37:14.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:37:14.643 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch348
2025-08-01 06:37:17.931 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 348/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 7.774e-04, size: 416, ETA: 1:35:14
2025-08-01 06:37:21.491 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 348/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, 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: 7.766e-04, size: 544, ETA: 1:35:10
2025-08-01 06:37:24.984 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 348/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 7.758e-04, size: 544, ETA: 1:35:06
2025-08-01 06:37:28.451 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 348/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 7.750e-04, size: 512, ETA: 1:35:03
2025-08-01 06:37:31.820 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 348/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 7.742e-04, size: 544, ETA: 1:34:59
2025-08-01 06:37:35.228 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 348/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, 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: 7.734e-04, size: 416, ETA: 1:34:56
2025-08-01 06:37:36.739 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:37:43.645 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:37:44.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:37:44.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4455
2025-08-01 06:37:44.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3785
2025-08-01 06:37:44.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2246
2025-08-01 06:37:44.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3495
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.225
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.350
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:37:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:37:44.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:37:44.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:37:44.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:37:44.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:37:44.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:37:44.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:37:45.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:37:45.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:37:46.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:37:46.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:37:47.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:37:47.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:37:48.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:37:48.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:37:49.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:37:49.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:37:49.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 06:37:49.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:37:49.164 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.84 ms, Average inference time: 8.21 ms

2025-08-01 06:37:49.166 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:37:49.291 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:37:49.370 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch349
2025-08-01 06:37:52.784 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 349/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 7.722e-04, size: 384, ETA: 1:34:50
2025-08-01 06:37:56.189 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 349/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.7, lr: 7.714e-04, size: 256, ETA: 1:34:47
2025-08-01 06:37:59.750 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 349/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 9.1, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.9, lr: 7.706e-04, size: 352, ETA: 1:34:43
2025-08-01 06:38:03.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 349/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.6, lr: 7.698e-04, size: 448, ETA: 1:34:39
2025-08-01 06:38:06.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 349/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, 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: 7.690e-04, size: 512, ETA: 1:34:36
2025-08-01 06:38:09.662 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 349/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.7, lr: 7.682e-04, size: 320, ETA: 1:34:32
2025-08-01 06:38:11.101 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:38:17.916 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:38:18.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:38:19.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4430
2025-08-01 06:38:19.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3900
2025-08-01 06:38:19.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2838
2025-08-01 06:38:19.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3723
2025-08-01 06:38:19.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:38:19.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:38:19.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.443
2025-08-01 06:38:19.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.284
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.372
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:38:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:38:19.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:38:20.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:38:20.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:38:21.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:38:22.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:38:22.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:38:23.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:38:24.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:38:24.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:38:25.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:38:25.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:38:25.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 06:38:25.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:38:25.701 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.86 ms, Average inference time: 8.30 ms

2025-08-01 06:38:25.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:38:25.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:38:25.866 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch350
2025-08-01 06:38:29.082 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 350/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 1.0, lr: 7.670e-04, size: 352, ETA: 1:34:27
2025-08-01 06:38:32.365 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 350/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 7.662e-04, size: 544, ETA: 1:34:23
2025-08-01 06:38:35.731 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 350/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 7.654e-04, size: 544, ETA: 1:34:19
2025-08-01 06:38:39.165 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 350/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 7.646e-04, size: 448, ETA: 1:34:16
2025-08-01 06:38:42.599 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 350/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.7, lr: 7.638e-04, size: 544, ETA: 1:34:12
2025-08-01 06:38:46.001 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 350/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 7.630e-04, size: 480, ETA: 1:34:09
2025-08-01 06:38:47.576 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:38:54.424 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:38:55.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:38:55.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4298
2025-08-01 06:38:55.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4117
2025-08-01 06:38:55.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2544
2025-08-01 06:38:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3653
2025-08-01 06:38:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:38:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:38:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-08-01 06:38:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-01 06:38:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-01 06:38:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.365
2025-08-01 06:38:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:38:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:38:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:38:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:38:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:38:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:38:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:38:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:38:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:38:56.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:38:57.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:38:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:38:58.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:38:58.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:38:59.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:38:59.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:39:00.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:39:01.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:39:01.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:39:01.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 06:39:01.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:39:01.062 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.86 ms, Average inference time: 8.32 ms

2025-08-01 06:39:01.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:39:01.156 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:39:01.297 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch351
2025-08-01 06:39:04.603 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 351/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.9, lr: 7.619e-04, size: 480, ETA: 1:34:03
2025-08-01 06:39:07.818 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 351/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.7, lr: 7.611e-04, size: 416, ETA: 1:33:59
2025-08-01 06:39:11.176 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 351/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 7.603e-04, size: 256, ETA: 1:33:56
2025-08-01 06:39:14.535 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 351/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.9, lr: 7.595e-04, size: 448, ETA: 1:33:52
2025-08-01 06:39:18.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 351/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 7.587e-04, size: 512, ETA: 1:33:49
2025-08-01 06:39:21.303 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 351/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 7.579e-04, size: 352, ETA: 1:33:45
2025-08-01 06:39:22.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:39:29.399 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:39:30.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:39:30.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4318
2025-08-01 06:39:30.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3628
2025-08-01 06:39:30.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1989
2025-08-01 06:39:30.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3312
2025-08-01 06:39:30.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:39:30.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:39:30.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 06:39:30.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-01 06:39:30.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.199
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.331
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:39:30.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:39:31.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:39:32.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:39:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:39:33.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:39:33.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:39:34.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:39:35.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:39:35.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:39:36.350 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:39:36.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:39:36.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 06:39:36.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:39:36.358 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.86 ms, Average inference time: 8.43 ms

2025-08-01 06:39:36.359 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:39:36.455 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:39:36.546 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch352
2025-08-01 06:39:39.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 352/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 7.567e-04, size: 352, ETA: 1:33:39
2025-08-01 06:39:42.932 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 352/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 7.559e-04, size: 416, ETA: 1:33:36
2025-08-01 06:39:46.278 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 352/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 7.551e-04, size: 416, ETA: 1:33:32
2025-08-01 06:39:49.706 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 352/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 7.543e-04, size: 576, ETA: 1:33:28
2025-08-01 06:39:52.898 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 352/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.0, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 3.3, cls_loss: 0.6, lr: 7.535e-04, size: 256, ETA: 1:33:25
2025-08-01 06:39:56.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 352/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 7.527e-04, size: 480, ETA: 1:33:21
2025-08-01 06:39:57.742 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:40:04.344 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:40:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:40:05.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5247
2025-08-01 06:40:05.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4375
2025-08-01 06:40:05.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3040
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4221
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.422
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:40:05.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:40:05.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:40:05.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:40:05.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:40:05.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:40:05.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:40:05.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:40:05.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:40:06.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:40:07.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:40:08.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:40:08.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:40:09.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:40:10.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:40:10.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:40:11.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:40:12.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:40:12.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 06:40:12.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 06:40:12.226 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:40:12.234 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.85 ms, Average inference time: 8.31 ms

2025-08-01 06:40:12.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:40:12.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:40:12.466 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch353
2025-08-01 06:40:15.497 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 353/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 7.515e-04, size: 352, ETA: 1:33:15
2025-08-01 06:40:18.804 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 353/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.8, lr: 7.507e-04, size: 512, ETA: 1:33:12
2025-08-01 06:40:22.184 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 353/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, 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: 7.499e-04, size: 544, ETA: 1:33:08
2025-08-01 06:40:25.486 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 353/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 7.491e-04, size: 384, ETA: 1:33:04
2025-08-01 06:40:28.796 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 353/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 10.3, iou_loss: 3.1, l1_loss: 1.4, conf_loss: 4.7, cls_loss: 1.1, lr: 7.483e-04, size: 576, ETA: 1:33:01
2025-08-01 06:40:32.164 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 353/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.9, lr: 7.475e-04, size: 512, ETA: 1:32:57
2025-08-01 06:40:33.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:40:40.341 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:40:41.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:40:42.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3760
2025-08-01 06:40:42.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3864
2025-08-01 06:40:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1690
2025-08-01 06:40:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3105
2025-08-01 06:40:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:40:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:40:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-01 06:40:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-08-01 06:40:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.310
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:40:43.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:40:44.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:40:45.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:40:46.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:40:46.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:40:47.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:40:48.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:40:49.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:40:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:40:51.770 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:40:51.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 06:40:51.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 06:40:51.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:40:51.779 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.90 ms, Average inference time: 8.32 ms

2025-08-01 06:40:51.780 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:40:51.858 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:40:51.941 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch354
2025-08-01 06:40:55.274 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 354/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 7.464e-04, size: 576, ETA: 1:32:52
2025-08-01 06:40:58.754 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 354/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 7.456e-04, size: 544, ETA: 1:32:48
2025-08-01 06:41:02.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 354/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, 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: 7.448e-04, size: 512, ETA: 1:32:45
2025-08-01 06:41:05.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 354/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 7.440e-04, size: 512, ETA: 1:32:41
2025-08-01 06:41:09.131 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 354/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 7.432e-04, size: 544, ETA: 1:32:38
2025-08-01 06:41:12.519 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 354/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 7.424e-04, size: 352, ETA: 1:32:34
2025-08-01 06:41:13.953 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:41:20.603 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:41:21.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:41:22.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3568
2025-08-01 06:41:22.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3090
2025-08-01 06:41:23.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1409
2025-08-01 06:41:23.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2689
2025-08-01 06:41:23.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:41:23.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:41:23.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-08-01 06:41:23.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-08-01 06:41:23.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.141
2025-08-01 06:41:23.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.269
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:41:23.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:41:24.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:41:25.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:41:26.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:41:27.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:41:28.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:41:29.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:41:30.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:41:31.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:41:32.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:41:32.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 06:41:32.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 06:41:32.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:41:32.891 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.82 ms, Average inference time: 8.24 ms

2025-08-01 06:41:32.892 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:41:32.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:41:33.051 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch355
2025-08-01 06:41:36.202 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 355/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 7.412e-04, size: 352, ETA: 1:32:29
2025-08-01 06:41:39.650 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 355/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 7.404e-04, size: 352, ETA: 1:32:25
2025-08-01 06:41:43.093 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 355/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 7.396e-04, size: 544, ETA: 1:32:21
2025-08-01 06:41:46.852 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 355/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 7.388e-04, size: 288, ETA: 1:32:18
2025-08-01 06:41:50.136 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 355/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.6, lr: 7.381e-04, size: 320, ETA: 1:32:14
2025-08-01 06:41:53.401 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 355/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 7.373e-04, size: 288, ETA: 1:32:11
2025-08-01 06:41:54.888 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:42:01.560 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:42:02.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:42:03.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5357
2025-08-01 06:42:03.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4664
2025-08-01 06:42:03.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3702
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4574
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.457
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:42:03.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:42:03.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:42:03.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:42:03.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:42:03.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:42:03.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:42:03.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:42:03.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:42:03.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:42:04.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:42:05.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:42:06.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:42:07.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:42:07.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:42:08.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:42:09.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:42:09.884 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:42:09.884 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-01 06:42:09.884 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-01 06:42:09.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:42:09.897 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.84 ms, Average inference time: 8.29 ms

2025-08-01 06:42:09.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:42:10.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:42:10.133 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch356
2025-08-01 06:42:13.436 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 356/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 7.361e-04, size: 544, ETA: 1:32:05
2025-08-01 06:42:16.797 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 356/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 1.0, lr: 7.353e-04, size: 320, ETA: 1:32:02
2025-08-01 06:42:20.083 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 356/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 7.345e-04, size: 320, ETA: 1:31:58
2025-08-01 06:42:23.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 356/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 7.337e-04, size: 416, ETA: 1:31:54
2025-08-01 06:42:26.846 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 356/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 7.329e-04, size: 576, ETA: 1:31:51
2025-08-01 06:42:30.113 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 356/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 7.321e-04, size: 448, ETA: 1:31:47
2025-08-01 06:42:31.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:42:38.490 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:42:39.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:42:39.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5184
2025-08-01 06:42:39.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4443
2025-08-01 06:42:39.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2943
2025-08-01 06:42:39.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4190
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.419
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:42:39.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:42:39.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:42:39.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:42:39.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:42:39.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:42:39.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:42:39.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:42:40.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:42:40.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:42:40.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:42:41.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:42:41.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:42:42.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:42:42.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:42:43.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:42:43.770 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:42:43.770 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 06:42:43.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 06:42:43.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:42:43.780 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.84 ms, Average inference time: 8.34 ms

2025-08-01 06:42:43.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:42:43.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:42:44.009 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch357
2025-08-01 06:42:47.288 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 357/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, 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: 7.310e-04, size: 448, ETA: 1:31:42
2025-08-01 06:42:50.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 357/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 7.302e-04, size: 320, ETA: 1:31:38
2025-08-01 06:42:53.986 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 357/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, 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: 7.294e-04, size: 352, ETA: 1:31:34
2025-08-01 06:42:57.413 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 357/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 7.286e-04, size: 320, ETA: 1:31:31
2025-08-01 06:43:00.693 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 357/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.6, lr: 7.278e-04, size: 576, ETA: 1:31:27
2025-08-01 06:43:03.959 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 357/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.5, lr: 7.270e-04, size: 416, ETA: 1:31:23
2025-08-01 06:43:05.455 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:43:12.000 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:43:12.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:43:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3918
2025-08-01 06:43:12.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3631
2025-08-01 06:43:12.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1855
2025-08-01 06:43:12.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3135
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:43:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:43:12.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:43:12.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:43:12.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:43:12.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:43:12.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:43:12.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:43:13.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:43:13.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:43:13.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:43:13.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:43:13.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:43:14.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:43:14.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:43:14.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:43:14.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 06:43:14.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 06:43:14.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:43:14.535 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.67 ms, Average inference time: 8.20 ms

2025-08-01 06:43:14.535 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:43:14.675 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:43:14.753 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch358
2025-08-01 06:43:18.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 358/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 10.5, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 5.7, cls_loss: 0.7, lr: 7.258e-04, size: 544, ETA: 1:31:18
2025-08-01 06:43:21.564 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 358/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, 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: 7.251e-04, size: 384, ETA: 1:31:14
2025-08-01 06:43:24.953 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 358/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 7.243e-04, size: 288, ETA: 1:31:11
2025-08-01 06:43:28.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 358/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 7.235e-04, size: 416, ETA: 1:31:07
2025-08-01 06:43:31.682 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 358/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 9.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 5.7, cls_loss: 0.7, lr: 7.227e-04, size: 288, ETA: 1:31:04
2025-08-01 06:43:35.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 358/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, 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: 7.219e-04, size: 352, ETA: 1:31:00
2025-08-01 06:43:36.509 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:43:43.405 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:43:44.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:43:44.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5200
2025-08-01 06:43:44.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4507
2025-08-01 06:43:44.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3274
2025-08-01 06:43:44.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4327
2025-08-01 06:43:44.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:43:44.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:43:44.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:43:44.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:43:45.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:43:46.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:43:46.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:43:47.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:43:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:43:48.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:43:49.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:43:50.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:43:50.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:43:50.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 06:43:50.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 06:43:50.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:43:50.981 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.88 ms, Average inference time: 8.37 ms

2025-08-01 06:43:50.983 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:43:51.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:43:51.149 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch359
2025-08-01 06:43:54.454 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 359/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.9, lr: 7.207e-04, size: 352, ETA: 1:30:55
2025-08-01 06:43:57.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 359/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.1, l1_loss: 1.6, conf_loss: 3.6, cls_loss: 0.9, lr: 7.199e-04, size: 480, ETA: 1:30:51
2025-08-01 06:44:01.135 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 359/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 0.7, cls_loss: 0.6, lr: 7.191e-04, size: 384, ETA: 1:30:47
2025-08-01 06:44:04.336 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 359/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 7.184e-04, size: 512, ETA: 1:30:43
2025-08-01 06:44:07.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 359/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.9, lr: 7.176e-04, size: 320, ETA: 1:30:40
2025-08-01 06:44:11.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 359/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 7.168e-04, size: 416, ETA: 1:30:36
2025-08-01 06:44:12.608 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:44:19.197 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:44:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:44:20.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4155
2025-08-01 06:44:20.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3545
2025-08-01 06:44:20.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2491
2025-08-01 06:44:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3397
2025-08-01 06:44:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:44:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:44:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-01 06:44:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-08-01 06:44:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.340
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:44:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:44:20.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:44:20.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:44:21.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:44:22.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:44:22.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:44:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:44:23.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:44:24.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:44:24.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:44:25.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:44:25.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:44:25.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 06:44:25.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:44:25.119 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.88 ms, Average inference time: 8.29 ms

2025-08-01 06:44:25.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:44:25.246 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:44:25.326 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch360
2025-08-01 06:44:28.623 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 360/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 2.8, iou_loss: 1.3, l1_loss: 0.3, conf_loss: 0.7, cls_loss: 0.5, lr: 7.156e-04, size: 288, ETA: 1:30:31
2025-08-01 06:44:31.872 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 360/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.3, cls_loss: 0.6, lr: 7.148e-04, size: 416, ETA: 1:30:27
2025-08-01 06:44:35.117 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 360/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.3, l1_loss: 1.6, conf_loss: 3.6, cls_loss: 0.9, lr: 7.140e-04, size: 576, ETA: 1:30:23
2025-08-01 06:44:38.574 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 360/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 7.132e-04, size: 544, ETA: 1:30:20
2025-08-01 06:44:42.031 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 360/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.6, lr: 7.125e-04, size: 288, ETA: 1:30:16
2025-08-01 06:44:45.262 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 360/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 7.117e-04, size: 512, ETA: 1:30:12
2025-08-01 06:44:46.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:44:53.483 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:44:53.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:44:54.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4444
2025-08-01 06:44:54.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4229
2025-08-01 06:44:54.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1726
2025-08-01 06:44:54.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3466
2025-08-01 06:44:54.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:44:54.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:44:54.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 06:44:54.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-08-01 06:44:54.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.173
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.347
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:44:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:44:54.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:44:54.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:44:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:44:55.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:44:55.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:44:56.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:44:56.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:44:56.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:44:57.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:44:57.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:44:57.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 06:44:57.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:44:57.266 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.79 ms, Average inference time: 8.26 ms

2025-08-01 06:44:57.267 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:44:57.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:44:57.482 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch361
2025-08-01 06:45:00.686 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 361/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 7.105e-04, size: 288, ETA: 1:30:07
2025-08-01 06:45:04.395 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 361/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 7.097e-04, size: 320, ETA: 1:30:04
2025-08-01 06:45:07.621 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 361/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.8, lr: 7.089e-04, size: 320, ETA: 1:30:00
2025-08-01 06:45:11.054 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 361/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 7.081e-04, size: 576, ETA: 1:29:56
2025-08-01 06:45:14.296 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 361/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, 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: 7.074e-04, size: 320, ETA: 1:29:53
2025-08-01 06:45:17.577 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 361/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 7.066e-04, size: 416, ETA: 1:29:49
2025-08-01 06:45:19.010 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:45:25.638 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:45:26.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:45:27.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4778
2025-08-01 06:45:27.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3998
2025-08-01 06:45:27.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2422
2025-08-01 06:45:27.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3732
2025-08-01 06:45:27.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:45:27.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:45:27.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-01 06:45:27.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 06:45:27.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 06:45:27.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.373
2025-08-01 06:45:27.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:45:27.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:45:27.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:45:27.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:45:27.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:45:27.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:45:27.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:45:27.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:45:27.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:45:28.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:45:29.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:45:30.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:45:31.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:45:32.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:45:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:45:34.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:45:35.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:45:36.734 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:45:36.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:45:36.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 06:45:36.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:45:36.748 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.89 ms, Average inference time: 8.30 ms

2025-08-01 06:45:36.749 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:45:36.861 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:45:36.935 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch362
2025-08-01 06:45:40.259 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 362/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 7.054e-04, size: 448, ETA: 1:29:44
2025-08-01 06:45:43.653 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 362/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 2.5, cls_loss: 0.9, lr: 7.046e-04, size: 576, ETA: 1:29:40
2025-08-01 06:45:47.157 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 362/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 7.038e-04, size: 480, ETA: 1:29:37
2025-08-01 06:45:50.377 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 362/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, 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: 7.031e-04, size: 416, ETA: 1:29:33
2025-08-01 06:45:53.568 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 362/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 7.023e-04, size: 320, ETA: 1:29:29
2025-08-01 06:45:57.038 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 362/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.2, iou_loss: 3.0, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 0.9, lr: 7.015e-04, size: 544, ETA: 1:29:26
2025-08-01 06:45:58.566 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:46:05.266 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:46:06.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:46:07.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4977
2025-08-01 06:46:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4098
2025-08-01 06:46:07.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3103
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4059
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.406
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:46:07.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:46:07.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:46:07.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:46:07.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:46:07.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:46:07.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:46:07.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:46:07.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:46:08.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:46:09.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:46:10.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:46:11.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:46:12.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:46:13.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:46:14.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:46:15.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:46:16.689 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:46:16.689 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:46:16.689 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:46:16.689 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:46:16.697 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.88 ms, Average inference time: 8.46 ms

2025-08-01 06:46:16.698 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:46:16.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:46:16.862 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch363
2025-08-01 06:46:20.419 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 363/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 7.003e-04, size: 512, ETA: 1:29:21
2025-08-01 06:46:23.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 363/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, 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: 6.996e-04, size: 416, ETA: 1:29:17
2025-08-01 06:46:27.024 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 363/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 6.988e-04, size: 320, ETA: 1:29:13
2025-08-01 06:46:30.248 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 363/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.8, lr: 6.980e-04, size: 352, ETA: 1:29:09
2025-08-01 06:46:33.720 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 363/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.8, lr: 6.972e-04, size: 480, ETA: 1:29:06
2025-08-01 06:46:37.262 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 363/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 8.9, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 1.0, lr: 6.964e-04, size: 576, ETA: 1:29:02
2025-08-01 06:46:38.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:46:45.771 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:46:46.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:46:47.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5144
2025-08-01 06:46:47.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4403
2025-08-01 06:46:47.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2699
2025-08-01 06:46:47.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4082
2025-08-01 06:46:47.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:46:47.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.408
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:46:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:46:47.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:46:48.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:46:48.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:46:49.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:46:49.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:46:50.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:46:51.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:46:51.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:46:52.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:46:53.203 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:46:53.203 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 06:46:53.203 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 06:46:53.203 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:46:53.210 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.87 ms, Average inference time: 8.43 ms

2025-08-01 06:46:53.212 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:46:53.292 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:46:53.373 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch364
2025-08-01 06:46:56.664 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 364/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 6.953e-04, size: 480, ETA: 1:28:57
2025-08-01 06:47:00.085 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 364/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 6.945e-04, size: 320, ETA: 1:28:54
2025-08-01 06:47:03.742 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 364/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 6.937e-04, size: 576, ETA: 1:28:50
2025-08-01 06:47:07.025 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 364/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 6.929e-04, size: 288, ETA: 1:28:47
2025-08-01 06:47:10.240 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 364/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 6.921e-04, size: 320, ETA: 1:28:43
2025-08-01 06:47:13.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 364/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 6.913e-04, size: 256, ETA: 1:28:39
2025-08-01 06:47:15.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:47:21.685 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:47:22.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:47:22.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3174
2025-08-01 06:47:22.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1587
2025-08-01 06:47:22.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1333
2025-08-01 06:47:22.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2031
2025-08-01 06:47:22.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.317
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.159
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.133
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.203
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:47:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:47:22.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:47:22.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:47:23.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:47:23.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:47:23.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:47:24.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:47:24.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:47:25.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:47:25.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:47:25.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:47:26.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:47:26.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 06:47:26.102 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.20
2025-08-01 06:47:26.102 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:47:26.109 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.87 ms, Average inference time: 8.33 ms

2025-08-01 06:47:26.111 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:47:26.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:47:26.273 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch365
2025-08-01 06:47:29.746 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 365/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.8, lr: 6.902e-04, size: 320, ETA: 1:28:34
2025-08-01 06:47:33.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 365/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 6.894e-04, size: 512, ETA: 1:28:30
2025-08-01 06:47:36.465 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 365/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 6.886e-04, size: 288, ETA: 1:28:27
2025-08-01 06:47:40.090 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 365/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 6.878e-04, size: 352, ETA: 1:28:23
2025-08-01 06:47:43.353 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 365/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 6.871e-04, size: 256, ETA: 1:28:20
2025-08-01 06:47:46.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 365/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 6.863e-04, size: 512, ETA: 1:28:16
2025-08-01 06:47:48.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:47:54.944 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:47:55.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:47:55.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4765
2025-08-01 06:47:56.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3307
2025-08-01 06:47:56.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2525
2025-08-01 06:47:56.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3532
2025-08-01 06:47:56.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.252
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:47:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:47:56.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:47:56.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:47:56.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:47:57.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:47:57.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:47:58.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:47:59.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:47:59.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:48:00.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:48:00.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:48:01.368 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:48:01.368 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:48:01.368 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 06:48:01.368 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:48:01.376 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.85 ms, Average inference time: 8.25 ms

2025-08-01 06:48:01.378 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:48:01.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:48:01.572 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch366
2025-08-01 06:48:04.836 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 366/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.5, lr: 6.851e-04, size: 320, ETA: 1:28:11
2025-08-01 06:48:08.047 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 366/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 6.844e-04, size: 352, ETA: 1:28:07
2025-08-01 06:48:11.256 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 366/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, 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: 6.836e-04, size: 352, ETA: 1:28:03
2025-08-01 06:48:14.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 366/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, 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: 6.828e-04, size: 288, ETA: 1:27:59
2025-08-01 06:48:18.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 366/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 6.820e-04, size: 448, ETA: 1:27:56
2025-08-01 06:48:21.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 366/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 6.812e-04, size: 352, ETA: 1:27:52
2025-08-01 06:48:23.080 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:48:29.738 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:48:30.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:48:30.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4880
2025-08-01 06:48:30.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4076
2025-08-01 06:48:31.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1793
2025-08-01 06:48:31.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3583
2025-08-01 06:48:31.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:48:31.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:48:31.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-01 06:48:31.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-01 06:48:31.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.179
2025-08-01 06:48:31.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.358
2025-08-01 06:48:31.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:48:31.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:48:31.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:48:31.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:48:31.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:48:31.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:48:31.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:48:31.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:48:31.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:48:31.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:48:32.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:48:32.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:48:33.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:48:33.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:48:34.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:48:34.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:48:35.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:48:36.063 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:48:36.064 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:48:36.064 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:48:36.064 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:48:36.071 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.88 ms, Average inference time: 8.40 ms

2025-08-01 06:48:36.073 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:48:36.205 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:48:36.286 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch367
2025-08-01 06:48:39.363 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 367/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 6.801e-04, size: 416, ETA: 1:27:47
2025-08-01 06:48:42.612 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 367/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, 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: 6.793e-04, size: 320, ETA: 1:27:43
2025-08-01 06:48:45.775 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 367/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 6.785e-04, size: 320, ETA: 1:27:39
2025-08-01 06:48:48.909 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 367/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.8, lr: 6.777e-04, size: 288, ETA: 1:27:36
2025-08-01 06:48:52.369 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 367/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, 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: 6.770e-04, size: 288, ETA: 1:27:32
2025-08-01 06:48:55.821 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 367/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 6.762e-04, size: 576, ETA: 1:27:29
2025-08-01 06:48:57.658 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:49:04.501 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:49:05.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:49:05.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4421
2025-08-01 06:49:05.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3921
2025-08-01 06:49:05.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2240
2025-08-01 06:49:05.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3527
2025-08-01 06:49:05.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:49:05.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:49:05.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:49:05.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:49:05.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:49:06.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:49:06.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:49:07.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:49:07.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:49:08.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:49:08.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:49:08.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:49:09.228 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:49:09.228 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:49:09.228 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 06:49:09.229 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:49:09.235 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.65 ms, Average NMS time: 0.80 ms, Average inference time: 8.45 ms

2025-08-01 06:49:09.236 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:49:09.332 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:49:09.425 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch368
2025-08-01 06:49:13.034 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 368/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.6, lr: 6.750e-04, size: 384, ETA: 1:27:24
2025-08-01 06:49:16.571 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 368/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.743e-04, size: 288, ETA: 1:27:20
2025-08-01 06:49:20.161 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 368/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 6.735e-04, size: 576, ETA: 1:27:17
2025-08-01 06:49:23.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 368/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, 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: 6.727e-04, size: 480, ETA: 1:27:13
2025-08-01 06:49:27.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 368/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 6.719e-04, size: 448, ETA: 1:27:10
2025-08-01 06:49:30.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 368/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 6.711e-04, size: 320, ETA: 1:27:06
2025-08-01 06:49:31.980 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:49:38.680 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:49:39.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:49:40.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5148
2025-08-01 06:49:40.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4940
2025-08-01 06:49:40.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2938
2025-08-01 06:49:40.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4342
2025-08-01 06:49:40.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:49:40.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:49:40.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-01 06:49:40.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-01 06:49:40.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:49:40.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:49:40.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:49:41.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:49:42.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:49:43.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:49:44.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:49:45.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:49:46.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:49:47.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:49:48.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:49:49.099 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:49:49.100 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:49:49.100 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 06:49:49.100 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:49:49.110 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.86 ms, Average inference time: 8.24 ms

2025-08-01 06:49:49.111 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:49:49.252 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:49:49.360 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch369
2025-08-01 06:49:52.685 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 369/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.6, lr: 6.700e-04, size: 576, ETA: 1:27:01
2025-08-01 06:49:56.254 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 369/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 16.2, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 16.2, cls_loss: 0.0, lr: 6.692e-04, size: 480, ETA: 1:26:57
2025-08-01 06:49:59.480 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 369/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.6, lr: 6.685e-04, size: 256, ETA: 1:26:54
2025-08-01 06:50:02.864 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 369/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 6.677e-04, size: 352, ETA: 1:26:50
2025-08-01 06:50:06.145 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 369/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.7, conf_loss: 2.1, cls_loss: 0.7, lr: 6.669e-04, size: 512, ETA: 1:26:46
2025-08-01 06:50:09.611 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 369/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 3.7, cls_loss: 0.9, lr: 6.661e-04, size: 576, ETA: 1:26:43
2025-08-01 06:50:11.398 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:50:18.114 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:50:18.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:50:18.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4517
2025-08-01 06:50:19.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3873
2025-08-01 06:50:19.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2186
2025-08-01 06:50:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3525
2025-08-01 06:50:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.219
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:50:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:50:19.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:50:19.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:50:19.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:50:19.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:50:19.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:50:19.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:50:20.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:50:20.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:50:21.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:50:21.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:50:22.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:50:22.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:50:23.080 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:50:23.080 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:50:23.080 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 06:50:23.080 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:50:23.088 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.85 ms, Average inference time: 8.36 ms

2025-08-01 06:50:23.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:50:23.217 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:50:23.299 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch370
2025-08-01 06:50:26.580 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 370/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, 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: 6.650e-04, size: 384, ETA: 1:26:38
2025-08-01 06:50:29.899 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 370/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 6.642e-04, size: 480, ETA: 1:26:34
2025-08-01 06:50:33.599 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 370/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 6.634e-04, size: 352, ETA: 1:26:31
2025-08-01 06:50:36.983 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 370/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, 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: 6.627e-04, size: 512, ETA: 1:26:27
2025-08-01 06:50:40.477 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 370/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 6.619e-04, size: 512, ETA: 1:26:23
2025-08-01 06:50:43.876 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 370/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 6.611e-04, size: 480, ETA: 1:26:20
2025-08-01 06:50:45.404 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:50:52.043 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:50:52.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:50:52.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5175
2025-08-01 06:50:52.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4544
2025-08-01 06:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3110
2025-08-01 06:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4276
2025-08-01 06:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:50:52.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:50:52.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:50:52.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:50:52.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:50:52.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:50:53.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:50:53.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:50:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:50:54.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:50:54.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:50:55.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:50:55.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:50:56.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:50:56.549 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:50:56.549 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-01 06:50:56.549 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 06:50:56.549 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:50:56.555 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.80 ms, Average inference time: 8.31 ms

2025-08-01 06:50:56.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:50:56.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:50:56.753 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch371
2025-08-01 06:51:00.128 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 371/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 3.9, cls_loss: 0.7, lr: 6.600e-04, size: 576, ETA: 1:26:15
2025-08-01 06:51:03.902 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 371/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.187s, data_time: 0.005s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 6.592e-04, size: 352, ETA: 1:26:11
2025-08-01 06:51:07.369 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 371/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, 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: 6.584e-04, size: 480, ETA: 1:26:08
2025-08-01 06:51:10.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 371/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 6.577e-04, size: 480, ETA: 1:26:04
2025-08-01 06:51:14.453 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 371/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.178s, 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: 6.569e-04, size: 544, ETA: 1:26:01
2025-08-01 06:51:17.911 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 371/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.9, lr: 6.561e-04, size: 480, ETA: 1:25:57
2025-08-01 06:51:19.384 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:51:26.029 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:51:26.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:51:26.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3791
2025-08-01 06:51:27.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3400
2025-08-01 06:51:27.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1789
2025-08-01 06:51:27.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2993
2025-08-01 06:51:27.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.179
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.299
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:51:27.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:51:27.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:51:27.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:51:27.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:51:28.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:51:28.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:51:28.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:51:29.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:51:29.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:51:30.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:51:30.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:51:31.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:51:31.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 06:51:31.220 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 06:51:31.220 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:51:31.232 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.84 ms, Average inference time: 8.25 ms

2025-08-01 06:51:31.233 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:51:31.352 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:51:31.501 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch372
2025-08-01 06:51:34.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 372/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 6.550e-04, size: 288, ETA: 1:25:52
2025-08-01 06:51:37.728 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 372/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 6.542e-04, size: 352, ETA: 1:25:48
2025-08-01 06:51:41.017 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 372/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.4, cls_loss: 0.8, lr: 6.534e-04, size: 512, ETA: 1:25:44
2025-08-01 06:51:44.414 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 372/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.6, lr: 6.527e-04, size: 544, ETA: 1:25:41
2025-08-01 06:51:47.734 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 372/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.6, lr: 6.519e-04, size: 480, ETA: 1:25:37
2025-08-01 06:51:51.236 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 372/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 6.511e-04, size: 576, ETA: 1:25:34
2025-08-01 06:51:52.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:51:59.485 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:52:00.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:52:00.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4699
2025-08-01 06:52:00.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4519
2025-08-01 06:52:01.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2359
2025-08-01 06:52:01.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3859
2025-08-01 06:52:01.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:52:01.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:52:01.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-08-01 06:52:01.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 06:52:01.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.386
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:52:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:52:01.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:52:01.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:52:02.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:52:03.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:52:03.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:52:04.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:52:05.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:52:05.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:52:06.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:52:07.102 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:52:07.102 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:52:07.102 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 06:52:07.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:52:07.112 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.85 ms, Average inference time: 8.32 ms

2025-08-01 06:52:07.113 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:52:07.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:52:07.366 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch373
2025-08-01 06:52:10.702 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 373/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 6.500e-04, size: 416, ETA: 1:25:28
2025-08-01 06:52:14.238 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 373/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 6.492e-04, size: 576, ETA: 1:25:25
2025-08-01 06:52:17.635 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 373/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 2.2, cls_loss: 0.4, lr: 6.484e-04, size: 384, ETA: 1:25:21
2025-08-01 06:52:20.988 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 373/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.7, lr: 6.477e-04, size: 256, ETA: 1:25:18
2025-08-01 06:52:24.517 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 373/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.7, lr: 6.469e-04, size: 480, ETA: 1:25:14
2025-08-01 06:52:27.783 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 373/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 6.461e-04, size: 256, ETA: 1:25:10
2025-08-01 06:52:29.387 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:52:36.127 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:52:37.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:52:37.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4548
2025-08-01 06:52:37.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3922
2025-08-01 06:52:37.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2097
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3522
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.210
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:52:37.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:52:37.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:52:37.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:52:37.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:52:37.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:52:37.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:52:37.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:52:38.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:52:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:52:39.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:52:40.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:52:41.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:52:41.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:52:42.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:52:43.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:52:43.722 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:52:43.722 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:52:43.722 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 06:52:43.722 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:52:43.730 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.84 ms, Average inference time: 8.36 ms

2025-08-01 06:52:43.731 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:52:43.817 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:52:43.898 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch374
2025-08-01 06:52:47.292 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 374/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 6.450e-04, size: 320, ETA: 1:25:05
2025-08-01 06:52:50.708 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 374/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 6.442e-04, size: 320, ETA: 1:25:02
2025-08-01 06:52:54.329 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 374/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.435e-04, size: 416, ETA: 1:24:58
2025-08-01 06:52:57.456 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 374/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 6.427e-04, size: 352, ETA: 1:24:55
2025-08-01 06:53:00.909 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 374/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 1.4, conf_loss: 2.0, cls_loss: 0.7, lr: 6.419e-04, size: 384, ETA: 1:24:51
2025-08-01 06:53:04.315 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 374/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.4, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 1.0, lr: 6.412e-04, size: 256, ETA: 1:24:47
2025-08-01 06:53:05.794 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:53:12.594 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:53:13.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:53:13.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3068
2025-08-01 06:53:13.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2363
2025-08-01 06:53:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1499
2025-08-01 06:53:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2310
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.150
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.231
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:53:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:53:13.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:53:13.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:53:13.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:53:13.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:53:13.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:53:13.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:53:14.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:53:14.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:53:14.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:53:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:53:15.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:53:15.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:53:16.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:53:16.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:53:16.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-08-01 06:53:16.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-08-01 06:53:16.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:53:16.525 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.83 ms, Average inference time: 8.31 ms

2025-08-01 06:53:16.528 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:53:16.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:53:16.714 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch375
2025-08-01 06:53:20.052 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 375/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 6.400e-04, size: 480, ETA: 1:24:42
2025-08-01 06:53:23.684 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 375/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.9, lr: 6.393e-04, size: 352, ETA: 1:24:39
2025-08-01 06:53:27.205 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 375/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, 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: 6.385e-04, size: 320, ETA: 1:24:35
2025-08-01 06:53:30.734 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 375/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 1.0, lr: 6.377e-04, size: 256, ETA: 1:24:32
2025-08-01 06:53:34.135 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 375/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 6.370e-04, size: 512, ETA: 1:24:28
2025-08-01 06:53:37.575 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 375/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 6.362e-04, size: 352, ETA: 1:24:25
2025-08-01 06:53:39.012 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:53:45.740 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:53:46.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:53:46.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4669
2025-08-01 06:53:47.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3908
2025-08-01 06:53:47.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2619
2025-08-01 06:53:47.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3732
2025-08-01 06:53:47.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:53:47.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:53:47.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-08-01 06:53:47.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-01 06:53:47.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.262
2025-08-01 06:53:47.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.373
2025-08-01 06:53:47.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:53:47.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:53:47.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:53:47.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:53:47.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:53:47.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:53:47.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:53:47.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:53:47.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:53:47.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:53:48.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:53:48.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:53:49.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:53:50.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:53:50.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:53:51.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:53:52.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:53:52.600 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:53:52.601 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 06:53:52.601 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 06:53:52.601 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:53:52.608 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.83 ms, Average inference time: 8.32 ms

2025-08-01 06:53:52.610 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:53:52.689 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:53:52.771 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch376
2025-08-01 06:53:56.044 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 376/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 6.351e-04, size: 448, ETA: 1:24:19
2025-08-01 06:53:59.692 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 376/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.6, lr: 6.343e-04, size: 320, ETA: 1:24:16
2025-08-01 06:54:02.985 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 376/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 6.335e-04, size: 288, ETA: 1:24:12
2025-08-01 06:54:06.325 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 376/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 6.328e-04, size: 320, ETA: 1:24:08
2025-08-01 06:54:09.611 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 376/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 6.320e-04, size: 448, ETA: 1:24:05
2025-08-01 06:54:12.820 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 376/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 6.312e-04, size: 288, ETA: 1:24:01
2025-08-01 06:54:14.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:54:21.342 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:54:22.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:54:22.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4813
2025-08-01 06:54:23.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4391
2025-08-01 06:54:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2418
2025-08-01 06:54:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3874
2025-08-01 06:54:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:54:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:54:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 06:54:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 06:54:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 06:54:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-08-01 06:54:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:54:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:54:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:54:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:54:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:54:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:54:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:54:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:54:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:54:23.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:54:24.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:54:24.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:54:25.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:54:26.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:54:26.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:54:27.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:54:27.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:54:28.643 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:54:28.643 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 06:54:28.643 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 06:54:28.643 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:54:28.654 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.88 ms, Average inference time: 8.27 ms

2025-08-01 06:54:28.655 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:54:28.772 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:54:28.855 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch377
2025-08-01 06:54:32.256 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 377/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 6.301e-04, size: 448, ETA: 1:23:56
2025-08-01 06:54:35.961 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 377/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 6.294e-04, size: 320, ETA: 1:23:53
2025-08-01 06:54:39.369 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 377/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 6.286e-04, size: 448, ETA: 1:23:49
2025-08-01 06:54:42.785 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 377/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 6.278e-04, size: 448, ETA: 1:23:45
2025-08-01 06:54:46.201 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 377/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.6, lr: 6.271e-04, size: 480, ETA: 1:23:42
2025-08-01 06:54:49.652 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 377/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.7, lr: 6.263e-04, size: 512, ETA: 1:23:38
2025-08-01 06:54:51.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:54:57.752 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:54:58.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:54:58.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2589
2025-08-01 06:54:58.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3079
2025-08-01 06:54:58.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0940
2025-08-01 06:54:58.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2203
2025-08-01 06:54:58.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:54:58.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:54:58.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.259
2025-08-01 06:54:58.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-08-01 06:54:58.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.094
2025-08-01 06:54:58.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.220
2025-08-01 06:54:58.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:54:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:54:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:54:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:54:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:54:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:54:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:54:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:54:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:54:58.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:54:59.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:54:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:54:59.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:54:59.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:55:00.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:55:00.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:55:00.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:55:01.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:55:01.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 06:55:01.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-08-01 06:55:01.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:55:01.125 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.78 ms, Average inference time: 8.23 ms

2025-08-01 06:55:01.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:55:01.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:55:01.316 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch378
2025-08-01 06:55:04.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 378/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 6.252e-04, size: 576, ETA: 1:23:33
2025-08-01 06:55:08.296 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 378/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 6.244e-04, size: 576, ETA: 1:23:30
2025-08-01 06:55:11.609 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 378/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 1.0, lr: 6.236e-04, size: 320, ETA: 1:23:26
2025-08-01 06:55:15.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 378/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 6.229e-04, size: 512, ETA: 1:23:22
2025-08-01 06:55:18.439 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 378/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 4.0, cls_loss: 0.8, lr: 6.221e-04, size: 416, ETA: 1:23:19
2025-08-01 06:55:21.757 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 378/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 6.214e-04, size: 320, ETA: 1:23:15
2025-08-01 06:55:23.288 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:55:30.025 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:55:30.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:55:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3069
2025-08-01 06:55:31.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2359
2025-08-01 06:55:31.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0641
2025-08-01 06:55:31.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2023
2025-08-01 06:55:31.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.064
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.202
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:55:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:55:31.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:55:31.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:55:31.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:55:32.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:55:32.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:55:33.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:55:33.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:55:34.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:55:35.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:55:35.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:55:36.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:55:36.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 06:55:36.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.20
2025-08-01 06:55:36.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:55:36.103 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.89 ms, Average inference time: 8.43 ms

2025-08-01 06:55:36.104 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:55:36.180 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:55:36.288 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch379
2025-08-01 06:55:39.458 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 379/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 2.6, cls_loss: 0.6, lr: 6.202e-04, size: 288, ETA: 1:23:10
2025-08-01 06:55:42.892 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 379/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.7, lr: 6.195e-04, size: 448, ETA: 1:23:06
2025-08-01 06:55:46.409 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 379/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.6, lr: 6.187e-04, size: 448, ETA: 1:23:03
2025-08-01 06:55:49.911 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 379/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 6.180e-04, size: 384, ETA: 1:22:59
2025-08-01 06:55:53.303 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 379/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 6.172e-04, size: 416, ETA: 1:22:56
2025-08-01 06:55:56.530 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 379/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.9, lr: 6.164e-04, size: 320, ETA: 1:22:52
2025-08-01 06:55:58.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:56:04.739 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:56:05.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:56:05.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3199
2025-08-01 06:56:05.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3404
2025-08-01 06:56:06.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2084
2025-08-01 06:56:06.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2896
2025-08-01 06:56:06.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:56:06.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:56:06.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.208
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.290
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:56:06.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:56:06.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:56:06.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:56:06.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:56:07.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:56:07.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:56:08.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:56:08.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:56:09.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:56:10.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:56:10.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:56:11.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:56:11.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 06:56:11.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 06:56:11.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:56:11.146 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.84 ms, Average inference time: 8.19 ms

2025-08-01 06:56:11.147 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:56:11.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:56:11.384 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch380
2025-08-01 06:56:14.557 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 380/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 6.153e-04, size: 416, ETA: 1:22:47
2025-08-01 06:56:18.218 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 380/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.182s, 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: 6.146e-04, size: 384, ETA: 1:22:43
2025-08-01 06:56:21.467 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 380/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 3.2, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.5, lr: 6.138e-04, size: 288, ETA: 1:22:40
2025-08-01 06:56:24.856 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 380/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 6.130e-04, size: 352, ETA: 1:22:36
2025-08-01 06:56:28.312 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 380/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 6.123e-04, size: 480, ETA: 1:22:32
2025-08-01 06:56:31.670 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 380/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.8, lr: 6.115e-04, size: 416, ETA: 1:22:29
2025-08-01 06:56:33.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:56:39.900 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:56:40.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:56:41.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4879
2025-08-01 06:56:41.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4217
2025-08-01 06:56:41.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2565
2025-08-01 06:56:41.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3887
2025-08-01 06:56:41.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:56:41.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.389
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:56:41.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:56:41.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:56:41.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:56:41.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:56:42.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:56:43.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:56:43.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:56:44.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:56:44.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:56:45.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:56:46.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:56:46.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:56:46.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 06:56:46.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 06:56:46.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:56:46.675 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.84 ms, Average inference time: 8.34 ms

2025-08-01 06:56:46.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:56:46.755 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:56:46.834 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch381
2025-08-01 06:56:50.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 381/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 8.7, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.6, cls_loss: 0.9, lr: 6.104e-04, size: 576, ETA: 1:22:24
2025-08-01 06:56:53.485 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 381/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 6.097e-04, size: 512, ETA: 1:22:20
2025-08-01 06:56:56.737 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 381/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 4.2, cls_loss: 0.9, lr: 6.089e-04, size: 416, ETA: 1:22:16
2025-08-01 06:57:00.310 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 381/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 6.081e-04, size: 352, ETA: 1:22:13
2025-08-01 06:57:03.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 381/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.5, lr: 6.074e-04, size: 448, ETA: 1:22:09
2025-08-01 06:57:07.214 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 381/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 6.066e-04, size: 544, ETA: 1:22:06
2025-08-01 06:57:08.677 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:57:15.812 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:57:16.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:57:17.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3789
2025-08-01 06:57:17.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3684
2025-08-01 06:57:17.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1808
2025-08-01 06:57:17.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3094
2025-08-01 06:57:17.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:57:17.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:57:17.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-01 06:57:17.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-01 06:57:17.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:57:17.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:57:17.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:57:18.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:57:19.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:57:19.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:57:20.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:57:20.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:57:21.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:57:22.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:57:22.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:57:22.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 06:57:22.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 06:57:22.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:57:22.755 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.87 ms, Average inference time: 8.40 ms

2025-08-01 06:57:22.756 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:57:22.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:57:22.976 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch382
2025-08-01 06:57:26.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 382/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.8, lr: 6.055e-04, size: 512, ETA: 1:22:01
2025-08-01 06:57:29.706 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 382/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 6.048e-04, size: 480, ETA: 1:21:57
2025-08-01 06:57:32.931 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 382/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 6.040e-04, size: 416, ETA: 1:21:53
2025-08-01 06:57:36.148 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 382/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 6.032e-04, size: 448, ETA: 1:21:50
2025-08-01 06:57:39.447 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 382/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.8, lr: 6.025e-04, size: 512, ETA: 1:21:46
2025-08-01 06:57:43.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 382/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 6.017e-04, size: 512, ETA: 1:21:43
2025-08-01 06:57:44.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:57:51.334 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:57:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:57:54.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4855
2025-08-01 06:57:54.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3748
2025-08-01 06:57:54.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2156
2025-08-01 06:57:54.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3586
2025-08-01 06:57:54.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:57:54.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:57:54.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-08-01 06:57:54.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-01 06:57:54.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-08-01 06:57:54.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-08-01 06:57:54.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:57:54.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:57:54.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:57:54.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:57:54.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:57:54.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:57:54.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:57:54.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:57:54.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:57:56.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:57:57.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:57:59.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:58:00.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:58:02.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:58:03.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:58:05.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:58:06.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:58:08.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:58:08.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 06:58:08.367 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 06:58:08.367 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:58:08.375 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 06:58:08.380 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:58:08.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:58:08.545 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch383
2025-08-01 06:58:11.726 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 383/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 9.7, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 6.3, cls_loss: 1.0, lr: 6.006e-04, size: 416, ETA: 1:21:37
2025-08-01 06:58:15.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 383/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 5.999e-04, size: 480, ETA: 1:21:34
2025-08-01 06:58:18.334 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 383/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 0.7, lr: 5.991e-04, size: 416, ETA: 1:21:30
2025-08-01 06:58:21.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 383/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 5.984e-04, size: 448, ETA: 1:21:26
2025-08-01 06:58:24.883 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 383/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 5.976e-04, size: 288, ETA: 1:21:23
2025-08-01 06:58:28.285 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 383/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.6, lr: 5.968e-04, size: 576, ETA: 1:21:19
2025-08-01 06:58:29.799 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:58:36.473 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:58:37.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:58:37.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5209
2025-08-01 06:58:37.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4252
2025-08-01 06:58:37.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3201
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4221
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.422
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:58:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:58:37.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:58:37.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:58:37.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:58:37.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:58:37.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:58:37.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:58:37.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:58:38.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:58:38.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:58:39.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:58:39.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:58:39.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:58:40.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:58:40.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:58:41.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:58:41.844 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:58:41.844 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 06:58:41.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 06:58:41.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:58:41.859 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.81 ms, Average inference time: 8.28 ms

2025-08-01 06:58:41.860 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:58:41.975 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:58:42.101 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch384
2025-08-01 06:58:45.348 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 384/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 5.958e-04, size: 576, ETA: 1:21:14
2025-08-01 06:58:48.956 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 384/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 5.950e-04, size: 448, ETA: 1:21:10
2025-08-01 06:58:52.329 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 384/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, 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: 5.942e-04, size: 448, ETA: 1:21:07
2025-08-01 06:58:55.755 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 384/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 5.935e-04, size: 320, ETA: 1:21:03
2025-08-01 06:58:59.126 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 384/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 5.927e-04, size: 544, ETA: 1:21:00
2025-08-01 06:59:02.441 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 384/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 5.920e-04, size: 384, ETA: 1:20:56
2025-08-01 06:59:03.882 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:59:10.566 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:59:11.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:59:11.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3445
2025-08-01 06:59:11.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3370
2025-08-01 06:59:11.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1961
2025-08-01 06:59:11.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2925
2025-08-01 06:59:11.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:59:11.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:59:11.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-01 06:59:11.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-08-01 06:59:11.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.196
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.293
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:59:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:59:12.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:59:13.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:59:13.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:59:14.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:59:14.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:59:15.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:59:16.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:59:16.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:59:17.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:59:17.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 06:59:17.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 06:59:17.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:59:17.362 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.89 ms, Average inference time: 8.24 ms

2025-08-01 06:59:17.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:59:17.441 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:59:17.521 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch385
2025-08-01 06:59:20.713 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 385/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 5.909e-04, size: 480, ETA: 1:20:51
2025-08-01 06:59:24.031 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 385/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 5.901e-04, size: 256, ETA: 1:20:47
2025-08-01 06:59:27.443 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 385/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 0.7, cls_loss: 0.5, lr: 5.894e-04, size: 416, ETA: 1:20:43
2025-08-01 06:59:30.828 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 385/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 5.886e-04, size: 352, ETA: 1:20:40
2025-08-01 06:59:33.987 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 385/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 14.4, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 14.4, cls_loss: 0.0, lr: 5.879e-04, size: 320, ETA: 1:20:36
2025-08-01 06:59:37.244 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 385/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 5.871e-04, size: 256, ETA: 1:20:32
2025-08-01 06:59:38.773 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:59:45.629 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 06:59:46.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 06:59:46.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3520
2025-08-01 06:59:46.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2841
2025-08-01 06:59:46.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1473
2025-08-01 06:59:46.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2611
2025-08-01 06:59:46.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 06:59:46.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 06:59:46.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-01 06:59:46.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.284
2025-08-01 06:59:46.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.147
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.261
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 06:59:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 06:59:47.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 06:59:47.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 06:59:48.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 06:59:48.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 06:59:49.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 06:59:49.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 06:59:50.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 06:59:50.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 06:59:51.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 06:59:51.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 06:59:51.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 06:59:51.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 06:59:51.412 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.87 ms, Average inference time: 8.48 ms

2025-08-01 06:59:51.416 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:59:51.502 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 06:59:51.580 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch386
2025-08-01 06:59:54.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 386/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 5.860e-04, size: 480, ETA: 1:20:27
2025-08-01 06:59:58.192 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 386/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.005s, total_loss: 3.5, iou_loss: 1.4, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 5.853e-04, size: 352, ETA: 1:20:24
2025-08-01 07:00:01.409 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 386/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 5.845e-04, size: 448, ETA: 1:20:20
2025-08-01 07:00:04.792 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 386/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.9, iou_loss: 3.9, l1_loss: 2.0, conf_loss: 1.9, cls_loss: 1.1, lr: 5.838e-04, size: 576, ETA: 1:20:16
2025-08-01 07:00:08.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 386/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, 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: 5.830e-04, size: 544, ETA: 1:20:13
2025-08-01 07:00:11.640 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 386/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 5.823e-04, size: 320, ETA: 1:20:09
2025-08-01 07:00:13.198 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:00:19.937 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:00:20.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:00:21.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4157
2025-08-01 07:00:21.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3577
2025-08-01 07:00:21.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1700
2025-08-01 07:00:21.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3145
2025-08-01 07:00:21.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:00:21.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:00:21.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 07:00:21.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-01 07:00:21.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.170
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.314
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:00:21.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:00:22.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:00:22.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:00:23.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:00:23.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:00:24.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:00:24.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:00:25.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:00:25.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:00:26.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:00:26.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 07:00:26.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 07:00:26.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:00:26.273 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.66 ms, Average NMS time: 0.89 ms, Average inference time: 8.55 ms

2025-08-01 07:00:26.274 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:00:26.390 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:00:26.470 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch387
2025-08-01 07:00:29.842 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 387/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 5.812e-04, size: 544, ETA: 1:20:04
2025-08-01 07:00:33.365 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 387/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.6, lr: 5.804e-04, size: 480, ETA: 1:20:01
2025-08-01 07:00:36.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 387/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.8, lr: 5.797e-04, size: 256, ETA: 1:19:57
2025-08-01 07:00:40.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 387/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.7, lr: 5.789e-04, size: 480, ETA: 1:19:53
2025-08-01 07:00:43.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 387/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 5.782e-04, size: 416, ETA: 1:19:50
2025-08-01 07:00:46.799 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 387/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 5.775e-04, size: 448, ETA: 1:19:46
2025-08-01 07:00:48.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:00:55.204 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:00:56.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:00:56.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4916
2025-08-01 07:00:56.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4532
2025-08-01 07:00:56.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2613
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4020
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.261
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.402
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:00:56.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:00:56.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:00:56.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:00:56.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:00:56.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:00:57.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:00:58.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:00:59.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:00:59.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:01:00.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:01:01.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:01:02.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:01:03.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:01:03.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:01:03.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:01:03.763 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 07:01:03.763 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:01:03.770 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.88 ms, Average inference time: 8.48 ms

2025-08-01 07:01:03.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:01:03.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:01:03.928 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch388
2025-08-01 07:01:07.028 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 388/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 5.764e-04, size: 480, ETA: 1:19:41
2025-08-01 07:01:10.495 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 388/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.004s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 0.7, lr: 5.756e-04, size: 256, ETA: 1:19:37
2025-08-01 07:01:13.774 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 388/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 5.749e-04, size: 384, ETA: 1:19:34
2025-08-01 07:01:17.177 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 388/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 5.741e-04, size: 448, ETA: 1:19:30
2025-08-01 07:01:20.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 388/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 1.0, lr: 5.734e-04, size: 384, ETA: 1:19:27
2025-08-01 07:01:23.967 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 388/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, 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: 5.726e-04, size: 576, ETA: 1:19:23
2025-08-01 07:01:25.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:01:32.215 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:01:32.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:01:33.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4812
2025-08-01 07:01:33.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4521
2025-08-01 07:01:33.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2126
2025-08-01 07:01:33.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3820
2025-08-01 07:01:33.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:01:33.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:01:33.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 07:01:33.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 07:01:33.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-08-01 07:01:33.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-08-01 07:01:33.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:01:33.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:01:33.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:01:33.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:01:33.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:01:33.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:01:33.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:01:33.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:01:33.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:01:33.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:01:34.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:01:35.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:01:35.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:01:35.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:01:36.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:01:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:01:37.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:01:37.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:01:37.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:01:37.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:01:37.134 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.78 ms, Average inference time: 8.27 ms

2025-08-01 07:01:37.135 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:01:37.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:01:37.363 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch389
2025-08-01 07:01:40.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 389/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 5.715e-04, size: 544, ETA: 1:19:18
2025-08-01 07:01:44.143 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 389/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 5.708e-04, size: 448, ETA: 1:19:14
2025-08-01 07:01:47.665 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 389/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 5.701e-04, size: 352, ETA: 1:19:11
2025-08-01 07:01:51.045 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 389/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.8, lr: 5.693e-04, size: 512, ETA: 1:19:07
2025-08-01 07:01:54.424 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 389/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.5, lr: 5.686e-04, size: 256, ETA: 1:19:04
2025-08-01 07:01:57.572 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 389/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.9, lr: 5.678e-04, size: 544, ETA: 1:19:00
2025-08-01 07:01:59.179 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:02:05.775 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:02:06.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:02:06.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3720
2025-08-01 07:02:06.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3152
2025-08-01 07:02:06.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1991
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2954
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.199
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.295
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:02:06.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:02:06.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:02:06.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:02:06.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:02:06.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:02:06.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:02:07.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:02:07.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:02:08.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:02:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:02:08.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:02:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:02:09.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:02:09.764 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:02:09.764 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 07:02:09.764 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 07:02:09.764 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:02:09.771 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.83 ms, Average inference time: 8.38 ms

2025-08-01 07:02:09.772 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:02:09.851 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:02:09.933 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch390
2025-08-01 07:02:13.170 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 390/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 5.667e-04, size: 352, ETA: 1:18:55
2025-08-01 07:02:16.466 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 390/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 5.660e-04, size: 288, ETA: 1:18:51
2025-08-01 07:02:19.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 390/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.6, lr: 5.653e-04, size: 352, ETA: 1:18:48
2025-08-01 07:02:22.924 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 390/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 11.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 7.3, cls_loss: 0.8, lr: 5.645e-04, size: 384, ETA: 1:18:44
2025-08-01 07:02:26.253 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 390/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 5.638e-04, size: 544, ETA: 1:18:40
2025-08-01 07:02:29.577 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 390/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.8, lr: 5.630e-04, size: 384, ETA: 1:18:37
2025-08-01 07:02:31.092 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:02:37.859 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:02:38.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:02:39.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2961
2025-08-01 07:02:39.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2796
2025-08-01 07:02:39.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0747
2025-08-01 07:02:39.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2168
2025-08-01 07:02:39.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.075
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.217
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:02:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:02:39.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:02:39.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:02:40.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:02:41.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:02:42.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:02:43.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:02:44.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:02:45.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:02:46.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:02:47.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:02:48.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:02:48.484 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 07:02:48.484 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-08-01 07:02:48.484 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:02:48.491 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.88 ms, Average inference time: 8.35 ms

2025-08-01 07:02:48.492 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:02:48.576 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:02:48.653 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch391
2025-08-01 07:02:52.110 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 391/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.9, lr: 5.620e-04, size: 352, ETA: 1:18:31
2025-08-01 07:02:55.709 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 391/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.7, lr: 5.612e-04, size: 288, ETA: 1:18:28
2025-08-01 07:02:58.971 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 391/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 5.605e-04, size: 352, ETA: 1:18:24
2025-08-01 07:03:02.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 391/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.6, lr: 5.597e-04, size: 544, ETA: 1:18:21
2025-08-01 07:03:05.647 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 391/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 5.590e-04, size: 256, ETA: 1:18:17
2025-08-01 07:03:09.281 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 391/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 5.582e-04, size: 352, ETA: 1:18:14
2025-08-01 07:03:10.761 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:03:17.520 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:03:18.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:03:19.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3996
2025-08-01 07:03:19.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3689
2025-08-01 07:03:19.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2058
2025-08-01 07:03:19.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3247
2025-08-01 07:03:19.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:03:19.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:03:19.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 07:03:19.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 07:03:19.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.206
2025-08-01 07:03:19.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.325
2025-08-01 07:03:19.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:03:19.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:03:19.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:03:19.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:03:19.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:03:19.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:03:19.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:03:19.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:03:19.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:03:20.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:03:21.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:03:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:03:23.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:03:24.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:03:24.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:03:25.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:03:26.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:03:27.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:03:27.658 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 07:03:27.658 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 07:03:27.658 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:03:27.665 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.90 ms, Average inference time: 8.33 ms

2025-08-01 07:03:27.667 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:03:27.744 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:03:27.823 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch392
2025-08-01 07:03:30.966 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 392/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 5.572e-04, size: 288, ETA: 1:18:09
2025-08-01 07:03:34.111 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 392/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.9, lr: 5.564e-04, size: 256, ETA: 1:18:05
2025-08-01 07:03:37.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 392/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 5.557e-04, size: 416, ETA: 1:18:01
2025-08-01 07:03:40.847 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 392/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 5.550e-04, size: 512, ETA: 1:17:58
2025-08-01 07:03:44.362 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 392/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.5, lr: 5.542e-04, size: 384, ETA: 1:17:54
2025-08-01 07:03:47.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 392/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.9, cls_loss: 0.6, lr: 5.535e-04, size: 256, ETA: 1:17:50
2025-08-01 07:03:48.884 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:03:55.608 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:03:56.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:03:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2945
2025-08-01 07:03:57.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2678
2025-08-01 07:03:57.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1783
2025-08-01 07:03:57.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2469
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.268
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.178
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.247
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:03:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:03:57.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:03:57.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:03:57.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:03:58.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:03:59.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:03:59.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:04:00.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:04:01.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:04:02.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:04:03.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:04:03.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:04:04.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:04:04.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 07:04:04.576 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-08-01 07:04:04.576 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:04:04.590 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.85 ms, Average inference time: 8.33 ms

2025-08-01 07:04:04.591 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:04:04.701 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:04:04.791 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch393
2025-08-01 07:04:08.002 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 393/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 5.524e-04, size: 416, ETA: 1:17:45
2025-08-01 07:04:11.311 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 393/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 0.6, lr: 5.517e-04, size: 256, ETA: 1:17:41
2025-08-01 07:04:14.852 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 393/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 5.509e-04, size: 576, ETA: 1:17:38
2025-08-01 07:04:18.171 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 393/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 5.502e-04, size: 288, ETA: 1:17:34
2025-08-01 07:04:21.428 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 393/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 5.495e-04, size: 320, ETA: 1:17:31
2025-08-01 07:04:25.181 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 393/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.5, lr: 5.487e-04, size: 544, ETA: 1:17:27
2025-08-01 07:04:26.838 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:04:33.499 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:04:34.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:04:34.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5024
2025-08-01 07:04:34.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3863
2025-08-01 07:04:35.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2673
2025-08-01 07:04:35.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3853
2025-08-01 07:04:35.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:04:35.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:04:35.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-01 07:04:35.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-08-01 07:04:35.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-01 07:04:35.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.385
2025-08-01 07:04:35.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:04:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:04:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:04:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:04:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:04:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:04:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:04:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:04:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:04:35.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:04:36.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:04:37.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:04:37.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:04:38.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:04:39.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:04:39.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:04:40.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:04:41.185 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:04:41.185 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:04:41.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:04:41.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:04:41.192 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.88 ms, Average inference time: 8.32 ms

2025-08-01 07:04:41.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:04:41.281 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:04:41.360 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch394
2025-08-01 07:04:44.755 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 394/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 5.476e-04, size: 416, ETA: 1:17:22
2025-08-01 07:04:48.243 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 394/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 5.469e-04, size: 576, ETA: 1:17:19
2025-08-01 07:04:51.746 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 394/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 5.462e-04, size: 352, ETA: 1:17:15
2025-08-01 07:04:55.001 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 394/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 5.454e-04, size: 416, ETA: 1:17:12
2025-08-01 07:04:58.538 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 394/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 5.447e-04, size: 448, ETA: 1:17:08
2025-08-01 07:05:01.954 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 394/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 5.440e-04, size: 480, ETA: 1:17:05
2025-08-01 07:05:03.492 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:05:10.328 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:05:11.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:05:11.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4417
2025-08-01 07:05:11.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3847
2025-08-01 07:05:11.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2114
2025-08-01 07:05:11.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3459
2025-08-01 07:05:11.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:05:11.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:05:11.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-01 07:05:11.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-01 07:05:11.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-01 07:05:11.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.346
2025-08-01 07:05:11.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:05:11.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:05:11.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:05:11.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:05:11.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:05:11.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:05:11.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:05:11.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:05:11.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:05:12.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:05:12.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:05:13.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:05:13.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:05:14.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:05:14.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:05:15.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:05:16.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:05:16.644 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:05:16.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:05:16.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 07:05:16.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:05:16.651 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.71 ms, Average NMS time: 0.87 ms, Average inference time: 8.58 ms

2025-08-01 07:05:16.658 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:05:16.736 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:05:16.815 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch395
2025-08-01 07:05:20.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 395/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 5.429e-04, size: 544, ETA: 1:16:59
2025-08-01 07:05:23.685 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 395/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 5.422e-04, size: 480, ETA: 1:16:56
2025-08-01 07:05:27.001 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 395/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 5.414e-04, size: 544, ETA: 1:16:52
2025-08-01 07:05:30.441 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 395/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 5.407e-04, size: 576, ETA: 1:16:49
2025-08-01 07:05:33.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 395/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.9, lr: 5.400e-04, size: 320, ETA: 1:16:45
2025-08-01 07:05:37.135 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 395/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 5.392e-04, size: 288, ETA: 1:16:42
2025-08-01 07:05:38.553 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:05:45.544 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:05:46.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:05:46.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2893
2025-08-01 07:05:46.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3043
2025-08-01 07:05:46.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1784
2025-08-01 07:05:46.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2573
2025-08-01 07:05:46.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:05:46.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:05:46.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-08-01 07:05:46.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-01 07:05:46.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.178
2025-08-01 07:05:46.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.257
2025-08-01 07:05:46.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:05:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:05:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:05:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:05:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:05:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:05:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:05:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:05:46.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:05:47.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:05:48.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:05:48.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:05:49.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:05:49.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:05:50.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:05:51.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:05:51.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:05:52.205 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:05:52.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 07:05:52.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 07:05:52.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:05:52.214 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.92 ms, Average inference time: 8.47 ms

2025-08-01 07:05:52.214 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:05:52.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:05:52.388 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch396
2025-08-01 07:05:55.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 396/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 5.382e-04, size: 256, ETA: 1:16:36
2025-08-01 07:05:59.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 396/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 5.374e-04, size: 384, ETA: 1:16:33
2025-08-01 07:06:02.505 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 396/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 5.367e-04, size: 576, ETA: 1:16:29
2025-08-01 07:06:06.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 396/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.4, conf_loss: 1.5, cls_loss: 0.6, lr: 5.360e-04, size: 384, ETA: 1:16:26
2025-08-01 07:06:09.287 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 396/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 5.352e-04, size: 288, ETA: 1:16:22
2025-08-01 07:06:12.541 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 396/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 1.7, conf_loss: 2.7, cls_loss: 0.7, lr: 5.345e-04, size: 512, ETA: 1:16:18
2025-08-01 07:06:14.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:06:20.910 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:06:21.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:06:22.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4562
2025-08-01 07:06:22.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3163
2025-08-01 07:06:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2336
2025-08-01 07:06:22.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3354
2025-08-01 07:06:22.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:06:22.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:06:22.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-01 07:06:22.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-01 07:06:22.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.234
2025-08-01 07:06:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.335
2025-08-01 07:06:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:06:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:06:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:06:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:06:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:06:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:06:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:06:22.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:06:22.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:06:23.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:06:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:06:24.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:06:25.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:06:25.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:06:26.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:06:27.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:06:27.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:06:28.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:06:28.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:06:28.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 07:06:28.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:06:28.602 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.90 ms, Average inference time: 8.42 ms

2025-08-01 07:06:28.603 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:06:28.680 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:06:28.813 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch397
2025-08-01 07:06:31.953 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 397/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.152s, 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: 5.335e-04, size: 512, ETA: 1:16:13
2025-08-01 07:06:35.269 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 397/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.6, lr: 5.327e-04, size: 512, ETA: 1:16:10
2025-08-01 07:06:38.625 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 397/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 5.320e-04, size: 416, ETA: 1:16:06
2025-08-01 07:06:42.156 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 397/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.8, lr: 5.313e-04, size: 576, ETA: 1:16:03
2025-08-01 07:06:45.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 397/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 5.305e-04, size: 384, ETA: 1:15:59
2025-08-01 07:06:49.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 397/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 5.298e-04, size: 352, ETA: 1:15:55
2025-08-01 07:06:50.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:06:57.220 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:06:58.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:06:59.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5364
2025-08-01 07:06:59.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4482
2025-08-01 07:06:59.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2917
2025-08-01 07:06:59.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4255
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.425
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:06:59.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:06:59.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:06:59.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:06:59.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:07:00.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:07:00.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:07:01.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:07:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:07:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:07:04.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:07:05.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:07:06.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:07:06.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:07:06.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 07:07:06.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 07:07:06.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:07:06.987 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.87 ms, Average inference time: 8.40 ms

2025-08-01 07:07:06.988 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:07:07.072 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:07:07.199 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch398
2025-08-01 07:07:10.502 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 398/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.8, lr: 5.288e-04, size: 576, ETA: 1:15:50
2025-08-01 07:07:14.111 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 398/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 5.280e-04, size: 544, ETA: 1:15:47
2025-08-01 07:07:17.502 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 398/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 5.273e-04, size: 352, ETA: 1:15:43
2025-08-01 07:07:20.733 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 398/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.7, lr: 5.266e-04, size: 256, ETA: 1:15:40
2025-08-01 07:07:24.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 398/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 5.258e-04, size: 544, ETA: 1:15:36
2025-08-01 07:07:27.508 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 398/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 1.1, lr: 5.251e-04, size: 544, ETA: 1:15:32
2025-08-01 07:07:29.031 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:07:35.870 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:07:36.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:07:37.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3921
2025-08-01 07:07:37.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3475
2025-08-01 07:07:37.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1843
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3079
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.308
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:07:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:07:37.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:07:37.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:07:37.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:07:37.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:07:37.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:07:38.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:07:38.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:07:39.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:07:39.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:07:40.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:07:40.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:07:41.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:07:41.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:07:41.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 07:07:41.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 07:07:41.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:07:41.861 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.86 ms, Average inference time: 8.30 ms

2025-08-01 07:07:41.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:07:41.946 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:07:42.027 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch399
2025-08-01 07:07:45.270 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 399/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.004s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 5.241e-04, size: 384, ETA: 1:15:27
2025-08-01 07:07:48.594 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 399/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.9, lr: 5.233e-04, size: 512, ETA: 1:15:24
2025-08-01 07:07:52.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 399/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 5.226e-04, size: 448, ETA: 1:15:20
2025-08-01 07:07:55.418 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 399/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 5.219e-04, size: 352, ETA: 1:15:16
2025-08-01 07:07:58.710 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 399/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 5.212e-04, size: 480, ETA: 1:15:13
2025-08-01 07:08:02.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 399/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 5.204e-04, size: 576, ETA: 1:15:09
2025-08-01 07:08:03.911 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:08:10.490 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:08:10.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:08:11.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4820
2025-08-01 07:08:11.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4103
2025-08-01 07:08:11.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2746
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3890
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.389
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:08:11.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:08:11.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:08:11.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:08:11.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:08:11.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:08:11.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:08:11.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:08:11.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:08:12.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:08:12.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:08:12.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:08:13.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:08:13.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:08:13.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:08:14.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:08:14.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:08:14.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:08:14.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:08:14.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:08:14.737 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.80 ms, Average inference time: 8.15 ms

2025-08-01 07:08:14.738 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:08:14.816 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:08:14.893 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch400
2025-08-01 07:08:18.100 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 400/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.9, lr: 5.194e-04, size: 384, ETA: 1:15:04
2025-08-01 07:08:21.333 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 400/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 5.187e-04, size: 416, ETA: 1:15:01
2025-08-01 07:08:24.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 400/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, 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: 5.179e-04, size: 576, ETA: 1:14:57
2025-08-01 07:08:28.386 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 400/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 5.172e-04, size: 256, ETA: 1:14:54
2025-08-01 07:08:31.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 400/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 5.165e-04, size: 480, ETA: 1:14:50
2025-08-01 07:08:35.037 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 400/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.7, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 3.0, cls_loss: 0.5, lr: 5.158e-04, size: 416, ETA: 1:14:46
2025-08-01 07:08:36.542 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:08:43.298 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:08:44.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:08:44.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4504
2025-08-01 07:08:44.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4305
2025-08-01 07:08:44.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1913
2025-08-01 07:08:45.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3574
2025-08-01 07:08:45.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:08:45.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:08:45.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-08-01 07:08:45.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-08-01 07:08:45.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 07:08:45.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.357
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:08:45.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:08:45.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:08:46.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:08:47.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:08:48.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:08:48.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:08:49.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:08:50.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:08:51.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:08:51.919 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:08:51.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:08:51.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 07:08:51.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:08:51.928 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.87 ms, Average inference time: 8.42 ms

2025-08-01 07:08:51.929 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:08:52.030 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:08:52.175 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch401
2025-08-01 07:08:55.488 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 401/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 5.147e-04, size: 512, ETA: 1:14:41
2025-08-01 07:08:58.696 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 401/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.8, lr: 5.140e-04, size: 256, ETA: 1:14:38
2025-08-01 07:09:02.287 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 401/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.8, lr: 5.133e-04, size: 512, ETA: 1:14:34
2025-08-01 07:09:05.656 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 401/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 5.126e-04, size: 352, ETA: 1:14:31
2025-08-01 07:09:08.857 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 401/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 5.118e-04, size: 320, ETA: 1:14:27
2025-08-01 07:09:12.180 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 401/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 5.111e-04, size: 384, ETA: 1:14:23
2025-08-01 07:09:13.593 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:09:20.346 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:09:21.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:09:21.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4067
2025-08-01 07:09:21.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3326
2025-08-01 07:09:21.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2637
2025-08-01 07:09:21.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3343
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.334
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:09:21.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:09:21.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:09:21.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:09:21.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:09:22.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:09:23.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:09:23.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:09:24.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:09:24.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:09:25.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:09:26.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:09:26.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:09:27.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:09:27.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 07:09:27.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 07:09:27.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:09:27.503 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.89 ms, Average inference time: 8.40 ms

2025-08-01 07:09:27.504 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:09:27.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:09:27.712 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch402
2025-08-01 07:09:31.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 402/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 5.2, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.8, lr: 5.101e-04, size: 512, ETA: 1:14:18
2025-08-01 07:09:34.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 402/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 5.094e-04, size: 384, ETA: 1:14:15
2025-08-01 07:09:37.641 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 402/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.6, lr: 5.086e-04, size: 352, ETA: 1:14:11
2025-08-01 07:09:40.810 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 402/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.5, lr: 5.079e-04, size: 352, ETA: 1:14:07
2025-08-01 07:09:44.170 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 402/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 5.072e-04, size: 416, ETA: 1:14:04
2025-08-01 07:09:47.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 402/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 5.065e-04, size: 352, ETA: 1:14:00
2025-08-01 07:09:49.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:09:56.047 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:09:56.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:09:57.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4333
2025-08-01 07:09:57.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4536
2025-08-01 07:09:57.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2095
2025-08-01 07:09:57.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3655
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.210
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.365
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:09:57.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:09:57.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:09:57.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:09:57.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:09:57.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:09:57.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:09:57.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:09:57.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:09:58.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:09:58.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:09:59.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:10:00.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:10:00.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:10:01.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:10:01.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:10:02.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:10:02.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:10:02.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 07:10:02.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:10:02.284 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.83 ms, Average inference time: 8.32 ms

2025-08-01 07:10:02.285 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:10:02.372 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:10:02.449 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch403
2025-08-01 07:10:05.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 403/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 1.0, lr: 5.054e-04, size: 320, ETA: 1:13:55
2025-08-01 07:10:09.088 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 403/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 5.047e-04, size: 256, ETA: 1:13:51
2025-08-01 07:10:12.335 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 403/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 5.040e-04, size: 288, ETA: 1:13:48
2025-08-01 07:10:15.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 403/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 5.033e-04, size: 480, ETA: 1:13:44
2025-08-01 07:10:18.990 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 403/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 5.026e-04, size: 384, ETA: 1:13:41
2025-08-01 07:10:22.359 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 403/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 5.019e-04, size: 416, ETA: 1:13:37
2025-08-01 07:10:23.786 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:10:30.385 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:10:31.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:10:31.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4721
2025-08-01 07:10:31.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4174
2025-08-01 07:10:31.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2175
2025-08-01 07:10:31.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3690
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:10:31.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:10:31.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:10:31.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:10:31.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:10:31.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:10:31.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:10:31.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:10:32.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:10:32.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:10:33.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:10:34.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:10:34.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:10:35.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:10:35.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:10:36.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:10:37.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:10:37.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:10:37.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 07:10:37.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:10:37.031 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.83 ms, Average inference time: 8.19 ms

2025-08-01 07:10:37.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:10:37.117 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:10:37.247 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch404
2025-08-01 07:10:40.628 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 404/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.7, lr: 5.008e-04, size: 416, ETA: 1:13:32
2025-08-01 07:10:43.987 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 404/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 5.001e-04, size: 320, ETA: 1:13:28
2025-08-01 07:10:47.414 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 404/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 4.994e-04, size: 480, ETA: 1:13:25
2025-08-01 07:10:50.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 404/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 4.987e-04, size: 448, ETA: 1:13:21
2025-08-01 07:10:54.298 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 404/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 4.980e-04, size: 576, ETA: 1:13:18
2025-08-01 07:10:57.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 404/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 4.973e-04, size: 480, ETA: 1:13:14
2025-08-01 07:10:59.140 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:11:05.762 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:11:06.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:11:06.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5104
2025-08-01 07:11:06.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4746
2025-08-01 07:11:06.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3381
2025-08-01 07:11:06.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4411
2025-08-01 07:11:06.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:11:06.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:11:06.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:11:06.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:11:07.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:11:07.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:11:08.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:11:08.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:11:09.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:11:09.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:11:10.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:11:10.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:11:11.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:11:11.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 07:11:11.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-01 07:11:11.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:11:11.178 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.84 ms, Average inference time: 8.37 ms

2025-08-01 07:11:11.179 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:11:11.262 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:11:11.340 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch405
2025-08-01 07:11:14.798 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 405/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 4.962e-04, size: 384, ETA: 1:13:09
2025-08-01 07:11:18.069 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 405/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 4.955e-04, size: 352, ETA: 1:13:05
2025-08-01 07:11:21.319 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 405/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 4.948e-04, size: 288, ETA: 1:13:02
2025-08-01 07:11:24.580 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 405/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.8, lr: 4.941e-04, size: 480, ETA: 1:12:58
2025-08-01 07:11:27.934 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 405/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 4.934e-04, size: 512, ETA: 1:12:55
2025-08-01 07:11:31.279 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 405/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, 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: 4.927e-04, size: 352, ETA: 1:12:51
2025-08-01 07:11:32.837 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:11:39.689 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:11:40.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:11:40.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3340
2025-08-01 07:11:40.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3519
2025-08-01 07:11:40.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1301
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2720
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.130
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.272
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:11:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:11:40.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:11:40.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:11:40.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:11:40.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:11:40.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:11:40.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:11:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:11:41.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:11:41.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:11:42.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:11:42.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:11:43.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:11:43.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:11:43.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:11:44.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:11:44.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 07:11:44.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 07:11:44.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:11:44.277 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.80 ms, Average inference time: 8.31 ms

2025-08-01 07:11:44.278 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:11:44.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:11:44.501 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch406
2025-08-01 07:11:47.867 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 406/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 4.916e-04, size: 288, ETA: 1:12:46
2025-08-01 07:11:51.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 406/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.4, lr: 4.909e-04, size: 352, ETA: 1:12:42
2025-08-01 07:11:54.255 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 406/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 4.902e-04, size: 416, ETA: 1:12:39
2025-08-01 07:11:57.749 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 406/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 4.895e-04, size: 256, ETA: 1:12:35
2025-08-01 07:12:01.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 406/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 4.888e-04, size: 352, ETA: 1:12:32
2025-08-01 07:12:04.486 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 406/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 4.881e-04, size: 352, ETA: 1:12:28
2025-08-01 07:12:05.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:12:12.731 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:12:13.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:12:14.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4864
2025-08-01 07:12:14.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4096
2025-08-01 07:12:14.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2388
2025-08-01 07:12:14.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3783
2025-08-01 07:12:14.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:12:14.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:12:14.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-01 07:12:14.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.239
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.378
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:12:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:12:14.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:12:15.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:12:16.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:12:17.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:12:17.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:12:18.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:12:19.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:12:19.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:12:20.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:12:20.672 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:12:20.672 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:12:20.672 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:12:20.682 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.32 ms, Average NMS time: 0.86 ms, Average inference time: 8.18 ms

2025-08-01 07:12:20.684 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:12:20.761 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:12:20.850 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch407
2025-08-01 07:12:24.202 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 407/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 4.870e-04, size: 576, ETA: 1:12:23
2025-08-01 07:12:27.724 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 407/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 4.863e-04, size: 576, ETA: 1:12:19
2025-08-01 07:12:31.005 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 407/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, 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: 4.856e-04, size: 352, ETA: 1:12:16
2025-08-01 07:12:34.412 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 407/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 4.849e-04, size: 384, ETA: 1:12:12
2025-08-01 07:12:37.495 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 407/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, 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: 0.6, lr: 4.842e-04, size: 288, ETA: 1:12:08
2025-08-01 07:12:40.719 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 407/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 4.835e-04, size: 416, ETA: 1:12:05
2025-08-01 07:12:42.275 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:12:49.024 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:12:49.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:12:49.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3650
2025-08-01 07:12:50.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3221
2025-08-01 07:12:50.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1423
2025-08-01 07:12:50.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2765
2025-08-01 07:12:50.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:12:50.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.142
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.276
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:12:50.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:12:50.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:12:50.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:12:50.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:12:50.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:12:50.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:12:51.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:12:51.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:12:52.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:12:52.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:12:53.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:12:53.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:12:54.329 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:12:54.329 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 07:12:54.329 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 07:12:54.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:12:54.338 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.79 ms, Average inference time: 8.29 ms

2025-08-01 07:12:54.339 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:12:54.421 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:12:54.499 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch408
2025-08-01 07:12:57.801 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 408/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, 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: 4.825e-04, size: 544, ETA: 1:12:00
2025-08-01 07:13:01.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 408/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 3.7, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.0, lr: 4.818e-04, size: 352, ETA: 1:11:56
2025-08-01 07:13:04.591 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 408/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 4.811e-04, size: 256, ETA: 1:11:53
2025-08-01 07:13:07.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 408/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.6, lr: 4.804e-04, size: 512, ETA: 1:11:49
2025-08-01 07:13:11.291 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 408/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.7, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.9, lr: 4.797e-04, size: 320, ETA: 1:11:45
2025-08-01 07:13:14.537 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 408/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 4.790e-04, size: 576, ETA: 1:11:42
2025-08-01 07:13:16.126 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:13:22.721 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:13:23.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:13:23.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2556
2025-08-01 07:13:23.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2103
2025-08-01 07:13:23.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0341
2025-08-01 07:13:23.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1667
2025-08-01 07:13:23.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:13:23.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:13:23.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.256
2025-08-01 07:13:23.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.210
2025-08-01 07:13:23.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.034
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.167
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:13:23.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:13:23.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:13:23.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:13:24.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:13:24.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:13:24.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:13:24.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:13:25.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:13:25.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:13:25.620 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:13:25.621 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-08-01 07:13:25.621 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.17
2025-08-01 07:13:25.621 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:13:25.626 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.66 ms, Average inference time: 8.07 ms

2025-08-01 07:13:25.627 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:13:25.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:13:25.799 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch409
2025-08-01 07:13:29.194 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 409/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 9.1, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 1.0, lr: 4.779e-04, size: 256, ETA: 1:11:37
2025-08-01 07:13:32.604 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 409/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 4.772e-04, size: 416, ETA: 1:11:33
2025-08-01 07:13:35.907 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 409/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 4.765e-04, size: 384, ETA: 1:11:30
2025-08-01 07:13:39.207 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 409/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 4.758e-04, size: 448, ETA: 1:11:26
2025-08-01 07:13:42.524 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 409/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, 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: 4.751e-04, size: 288, ETA: 1:11:22
2025-08-01 07:13:45.657 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 409/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, 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: 4.744e-04, size: 320, ETA: 1:11:19
2025-08-01 07:13:47.127 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:13:53.885 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:13:54.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:13:54.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3815
2025-08-01 07:13:54.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3935
2025-08-01 07:13:54.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1907
2025-08-01 07:13:54.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3219
2025-08-01 07:13:54.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:13:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:13:54.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:13:54.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:13:54.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:13:55.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:13:55.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:13:55.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:13:56.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:13:56.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:13:57.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:13:57.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:13:57.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:13:58.344 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:13:58.344 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 07:13:58.344 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 07:13:58.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:13:58.354 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.82 ms, Average inference time: 8.39 ms

2025-08-01 07:13:58.354 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:13:58.472 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:13:58.587 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch410
2025-08-01 07:14:01.714 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 410/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 4.734e-04, size: 256, ETA: 1:11:14
2025-08-01 07:14:05.025 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 410/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 4.727e-04, size: 544, ETA: 1:11:10
2025-08-01 07:14:08.615 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 410/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 4.720e-04, size: 416, ETA: 1:11:07
2025-08-01 07:14:11.996 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 410/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 4.713e-04, size: 480, ETA: 1:11:03
2025-08-01 07:14:15.460 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 410/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 4.706e-04, size: 512, ETA: 1:10:59
2025-08-01 07:14:18.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 410/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 4.699e-04, size: 320, ETA: 1:10:56
2025-08-01 07:14:20.427 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:14:27.151 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:14:27.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:14:28.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4475
2025-08-01 07:14:28.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3843
2025-08-01 07:14:28.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1839
2025-08-01 07:14:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3386
2025-08-01 07:14:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:14:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:14:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 07:14:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-01 07:14:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-08-01 07:14:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.339
2025-08-01 07:14:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:14:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:14:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:14:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:14:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:14:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:14:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:14:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:14:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:14:29.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:14:30.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:14:30.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:14:31.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:14:32.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:14:32.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:14:33.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:14:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:14:34.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:14:34.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 07:14:34.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 07:14:34.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:14:34.850 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.87 ms, Average inference time: 8.39 ms

2025-08-01 07:14:34.851 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:14:34.936 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:14:35.067 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch411
2025-08-01 07:14:38.163 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 411/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.152s, 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: 4.689e-04, size: 256, ETA: 1:10:51
2025-08-01 07:14:41.308 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 411/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 1.0, lr: 4.682e-04, size: 256, ETA: 1:10:47
2025-08-01 07:14:44.722 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 411/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.5, lr: 4.675e-04, size: 544, ETA: 1:10:44
2025-08-01 07:14:48.357 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 411/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 4.668e-04, size: 576, ETA: 1:10:40
2025-08-01 07:14:51.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 411/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 4.661e-04, size: 512, ETA: 1:10:37
2025-08-01 07:14:55.177 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 411/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 4.654e-04, size: 384, ETA: 1:10:33
2025-08-01 07:14:56.641 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:15:03.643 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:15:04.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:15:05.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4543
2025-08-01 07:15:05.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4386
2025-08-01 07:15:05.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2368
2025-08-01 07:15:05.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3766
2025-08-01 07:15:05.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:15:05.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:15:05.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 07:15:05.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 07:15:05.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.237
2025-08-01 07:15:05.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.377
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:15:05.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:15:06.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:15:07.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:15:07.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:15:08.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:15:09.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:15:10.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:15:10.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:15:11.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:15:12.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:15:12.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:15:12.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:15:12.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:15:12.423 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.88 ms, Average inference time: 8.32 ms

2025-08-01 07:15:12.424 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:15:12.540 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:15:12.620 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch412
2025-08-01 07:15:15.798 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 412/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.8, lr: 4.644e-04, size: 416, ETA: 1:10:28
2025-08-01 07:15:19.117 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 412/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 4.637e-04, size: 416, ETA: 1:10:24
2025-08-01 07:15:22.493 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 412/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 4.630e-04, size: 576, ETA: 1:10:21
2025-08-01 07:15:25.928 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 412/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.6, lr: 4.623e-04, size: 544, ETA: 1:10:17
2025-08-01 07:15:29.323 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 412/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 4.616e-04, size: 416, ETA: 1:10:14
2025-08-01 07:15:32.735 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 412/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, 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: 4.609e-04, size: 288, ETA: 1:10:10
2025-08-01 07:15:34.296 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:15:41.246 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:15:41.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:15:42.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5206
2025-08-01 07:15:42.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4070
2025-08-01 07:15:42.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3038
2025-08-01 07:15:42.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4105
2025-08-01 07:15:42.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:15:42.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:15:42.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-01 07:15:42.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 07:15:42.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.410
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:15:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:15:42.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:15:43.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:15:43.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:15:44.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:15:44.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:15:45.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:15:45.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:15:46.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:15:46.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:15:47.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:15:47.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 07:15:47.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 07:15:47.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:15:47.412 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.87 ms, Average inference time: 8.36 ms

2025-08-01 07:15:47.413 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:15:47.508 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:15:47.601 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch413
2025-08-01 07:15:51.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 413/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 2.2, cls_loss: 0.6, lr: 4.599e-04, size: 576, ETA: 1:10:05
2025-08-01 07:15:54.507 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 413/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.7, lr: 4.592e-04, size: 480, ETA: 1:10:02
2025-08-01 07:15:57.812 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 413/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 4.585e-04, size: 416, ETA: 1:09:58
2025-08-01 07:16:01.230 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 413/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 1.7, cls_loss: 0.7, lr: 4.578e-04, size: 448, ETA: 1:09:54
2025-08-01 07:16:04.653 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 413/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 4.571e-04, size: 576, ETA: 1:09:51
2025-08-01 07:16:08.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 413/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.8, lr: 4.564e-04, size: 544, ETA: 1:09:47
2025-08-01 07:16:09.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:16:16.246 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:16:16.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:16:17.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5039
2025-08-01 07:16:17.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4695
2025-08-01 07:16:17.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2877
2025-08-01 07:16:17.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4204
2025-08-01 07:16:17.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:16:17.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:16:17.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-01 07:16:17.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.420
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:16:17.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:16:17.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:16:18.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:16:18.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:16:19.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:16:19.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:16:20.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:16:20.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:16:21.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:16:21.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:16:22.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:16:22.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 07:16:22.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 07:16:22.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:16:22.400 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.82 ms, Average inference time: 8.30 ms

2025-08-01 07:16:22.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:16:22.545 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:16:22.716 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch414
2025-08-01 07:16:26.079 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 414/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.6, lr: 4.554e-04, size: 576, ETA: 1:09:42
2025-08-01 07:16:29.586 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 414/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 2.4, cls_loss: 0.7, lr: 4.547e-04, size: 576, ETA: 1:09:39
2025-08-01 07:16:33.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 414/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 4.540e-04, size: 448, ETA: 1:09:35
2025-08-01 07:16:36.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 414/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 4.533e-04, size: 480, ETA: 1:09:32
2025-08-01 07:16:40.052 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 414/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 4.526e-04, size: 544, ETA: 1:09:28
2025-08-01 07:16:43.419 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 414/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 4.520e-04, size: 544, ETA: 1:09:25
2025-08-01 07:16:44.997 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:16:51.947 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:16:53.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:16:53.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4604
2025-08-01 07:16:53.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3590
2025-08-01 07:16:54.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2301
2025-08-01 07:16:54.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3498
2025-08-01 07:16:54.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:16:54.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:16:54.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-01 07:16:54.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.230
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.350
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:16:54.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:16:54.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:16:54.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:16:55.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:16:56.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:16:57.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:16:57.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:16:58.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:16:59.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:17:00.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:17:00.899 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:17:00.899 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 07:17:00.899 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 07:17:00.899 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:17:00.907 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.87 ms, Average inference time: 8.34 ms

2025-08-01 07:17:00.908 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:17:00.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:17:01.069 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch415
2025-08-01 07:17:04.450 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 415/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, 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: 4.510e-04, size: 576, ETA: 1:09:20
2025-08-01 07:17:08.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 415/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.2, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 0.8, lr: 4.503e-04, size: 320, ETA: 1:09:16
2025-08-01 07:17:11.466 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 415/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 4.496e-04, size: 256, ETA: 1:09:13
2025-08-01 07:17:14.594 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 415/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.8, lr: 4.489e-04, size: 256, ETA: 1:09:09
2025-08-01 07:17:18.073 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 415/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 4.482e-04, size: 576, ETA: 1:09:05
2025-08-01 07:17:21.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 415/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.8, lr: 4.475e-04, size: 352, ETA: 1:09:02
2025-08-01 07:17:22.944 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:17:29.821 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:17:30.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:17:31.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4394
2025-08-01 07:17:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3992
2025-08-01 07:17:31.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3001
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3796
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:17:31.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:17:31.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:17:31.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:17:31.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:17:31.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:17:31.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:17:31.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:17:31.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:17:32.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:17:32.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:17:33.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:17:33.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:17:34.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:17:35.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:17:35.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:17:36.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:17:37.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:17:37.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:17:37.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:17:37.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:17:37.110 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.87 ms, Average inference time: 8.38 ms

2025-08-01 07:17:37.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:17:37.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:17:37.270 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch416
2025-08-01 07:17:40.603 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 416/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 4.465e-04, size: 544, ETA: 1:08:57
2025-08-01 07:17:43.866 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 416/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.5, lr: 4.458e-04, size: 480, ETA: 1:08:53
2025-08-01 07:17:47.246 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 416/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 4.451e-04, size: 544, ETA: 1:08:50
2025-08-01 07:17:50.758 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 416/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 4.445e-04, size: 352, ETA: 1:08:46
2025-08-01 07:17:53.952 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 416/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.8, lr: 4.438e-04, size: 256, ETA: 1:08:43
2025-08-01 07:17:57.471 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 416/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, 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: 4.431e-04, size: 544, ETA: 1:08:39
2025-08-01 07:17:59.005 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:18:05.864 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:18:06.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:18:07.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3881
2025-08-01 07:18:07.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3182
2025-08-01 07:18:07.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1755
2025-08-01 07:18:07.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2939
2025-08-01 07:18:07.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:18:07.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:18:07.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-01 07:18:07.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-01 07:18:07.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.176
2025-08-01 07:18:07.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.294
2025-08-01 07:18:07.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:18:07.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:18:07.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:18:07.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:18:07.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:18:07.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:18:07.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:18:07.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:18:07.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:18:08.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:18:08.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:18:09.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:18:10.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:18:10.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:18:11.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:18:12.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:18:12.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:18:13.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:18:13.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 07:18:13.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 07:18:13.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:18:13.588 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.87 ms, Average inference time: 8.38 ms

2025-08-01 07:18:13.589 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:18:13.671 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:18:13.751 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch417
2025-08-01 07:18:16.964 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 417/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 4.421e-04, size: 576, ETA: 1:08:34
2025-08-01 07:18:20.325 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 417/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 4.414e-04, size: 320, ETA: 1:08:30
2025-08-01 07:18:23.724 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 417/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 4.407e-04, size: 480, ETA: 1:08:27
2025-08-01 07:18:27.126 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 417/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 4.400e-04, size: 352, ETA: 1:08:23
2025-08-01 07:18:30.476 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 417/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 4.393e-04, size: 416, ETA: 1:08:20
2025-08-01 07:18:33.729 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 417/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 4.387e-04, size: 352, ETA: 1:08:16
2025-08-01 07:18:35.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:18:41.881 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:18:42.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:18:42.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5223
2025-08-01 07:18:43.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4622
2025-08-01 07:18:43.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2631
2025-08-01 07:18:43.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4159
2025-08-01 07:18:43.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:18:43.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:18:43.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-01 07:18:43.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-01 07:18:43.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-08-01 07:18:43.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.416
2025-08-01 07:18:43.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:18:43.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:18:43.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:18:43.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:18:43.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:18:43.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:18:43.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:18:43.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:18:43.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:18:43.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:18:44.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:18:44.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:18:45.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:18:45.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:18:46.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:18:46.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:18:47.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:18:47.944 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:18:47.944 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 07:18:47.944 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 07:18:47.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:18:47.951 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.82 ms, Average inference time: 8.31 ms

2025-08-01 07:18:47.953 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:18:48.030 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:18:48.108 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch418
2025-08-01 07:18:51.312 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 418/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.004s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 4.377e-04, size: 288, ETA: 1:08:11
2025-08-01 07:18:54.910 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 418/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.9, lr: 4.370e-04, size: 576, ETA: 1:08:07
2025-08-01 07:18:58.513 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 418/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 4.363e-04, size: 416, ETA: 1:08:04
2025-08-01 07:19:02.008 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 418/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 4.356e-04, size: 448, ETA: 1:08:00
2025-08-01 07:19:05.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 418/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 5.5, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 3.0, cls_loss: 0.5, lr: 4.349e-04, size: 544, ETA: 1:07:57
2025-08-01 07:19:09.242 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 418/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.178s, data_time: 0.005s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 4.343e-04, size: 448, ETA: 1:07:54
2025-08-01 07:19:10.847 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:19:17.653 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:19:18.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:19:19.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5267
2025-08-01 07:19:19.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4813
2025-08-01 07:19:19.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3091
2025-08-01 07:19:19.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4391
2025-08-01 07:19:19.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:19:19.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:19:19.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-01 07:19:19.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 07:19:19.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:19:19.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:19:19.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:19:20.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:19:20.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:19:21.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:19:22.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:19:23.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:19:23.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:19:24.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:19:25.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:19:25.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:19:25.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:19:25.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-01 07:19:25.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:19:25.829 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.65 ms, Average NMS time: 0.88 ms, Average inference time: 8.53 ms

2025-08-01 07:19:25.832 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:19:25.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:19:26.089 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch419
2025-08-01 07:19:29.457 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 419/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 4.333e-04, size: 416, ETA: 1:07:49
2025-08-01 07:19:32.690 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 419/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.8, lr: 4.326e-04, size: 448, ETA: 1:07:45
2025-08-01 07:19:36.178 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 419/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 4.319e-04, size: 288, ETA: 1:07:41
2025-08-01 07:19:39.364 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 419/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.5, lr: 4.312e-04, size: 448, ETA: 1:07:38
2025-08-01 07:19:42.714 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 419/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 4.306e-04, size: 320, ETA: 1:07:34
2025-08-01 07:19:46.034 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 419/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 1.0, lr: 4.299e-04, size: 256, ETA: 1:07:31
2025-08-01 07:19:47.457 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:19:54.429 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:19:55.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:19:55.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3805
2025-08-01 07:19:55.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2174
2025-08-01 07:19:56.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1564
2025-08-01 07:19:56.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2514
2025-08-01 07:19:56.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:19:56.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:19:56.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-01 07:19:56.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.156
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.251
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:19:56.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:19:56.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:19:56.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:19:57.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:19:58.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:19:58.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:19:59.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:20:00.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:20:00.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:20:01.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:20:02.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:20:02.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 07:20:02.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-08-01 07:20:02.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:20:02.219 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.91 ms, Average inference time: 8.26 ms

2025-08-01 07:20:02.229 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:20:02.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:20:02.390 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch420
2025-08-01 07:20:05.796 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 420/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 1.1, lr: 4.289e-04, size: 576, ETA: 1:07:26
2025-08-01 07:20:09.438 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 420/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 4.282e-04, size: 256, ETA: 1:07:22
2025-08-01 07:20:12.685 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 420/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.9, lr: 4.275e-04, size: 256, ETA: 1:07:19
2025-08-01 07:20:16.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 420/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 3.0, cls_loss: 0.6, lr: 4.269e-04, size: 416, ETA: 1:07:15
2025-08-01 07:20:19.524 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 420/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.9, lr: 4.262e-04, size: 256, ETA: 1:07:11
2025-08-01 07:20:23.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 420/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, 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: 4.255e-04, size: 288, ETA: 1:07:08
2025-08-01 07:20:24.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:20:31.300 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:20:32.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:20:32.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4743
2025-08-01 07:20:32.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3751
2025-08-01 07:20:32.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2536
2025-08-01 07:20:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3677
2025-08-01 07:20:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:20:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:20:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-01 07:20:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-01 07:20:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-01 07:20:32.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.368
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:20:32.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:20:33.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:20:34.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:20:34.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:20:35.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:20:36.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:20:36.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:20:37.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:20:38.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:20:38.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:20:38.728 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:20:38.728 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 07:20:38.728 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:20:38.736 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.88 ms, Average inference time: 8.30 ms

2025-08-01 07:20:38.737 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:20:38.823 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:20:38.902 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch421
2025-08-01 07:20:42.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 421/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 4.245e-04, size: 352, ETA: 1:07:03
2025-08-01 07:20:45.421 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 421/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 4.239e-04, size: 320, ETA: 1:06:59
2025-08-01 07:20:48.660 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 421/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 4.232e-04, size: 416, ETA: 1:06:56
2025-08-01 07:20:51.980 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 421/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 4.225e-04, size: 544, ETA: 1:06:52
2025-08-01 07:20:55.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 421/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.5, lr: 4.218e-04, size: 576, ETA: 1:06:49
2025-08-01 07:20:58.939 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 421/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 4.212e-04, size: 352, ETA: 1:06:45
2025-08-01 07:21:00.522 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:21:07.145 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:21:07.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:21:07.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5015
2025-08-01 07:21:07.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4346
2025-08-01 07:21:07.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2480
2025-08-01 07:21:07.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3947
2025-08-01 07:21:07.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:21:07.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:21:07.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.248
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:21:07.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:21:07.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:21:08.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:21:08.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:21:09.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:21:09.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:21:10.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:21:10.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:21:10.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:21:11.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:21:11.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:21:11.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:21:11.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:21:11.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:21:11.685 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.76 ms, Average inference time: 8.20 ms

2025-08-01 07:21:11.686 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:21:11.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:21:11.842 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch422
2025-08-01 07:21:15.415 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 422/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.8, lr: 4.202e-04, size: 448, ETA: 1:06:40
2025-08-01 07:21:18.669 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 422/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.3, iou_loss: 1.5, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 1.7, lr: 4.195e-04, size: 352, ETA: 1:06:36
2025-08-01 07:21:21.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 422/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, 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: 4.188e-04, size: 416, ETA: 1:06:33
2025-08-01 07:21:25.203 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 422/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 4.182e-04, size: 352, ETA: 1:06:29
2025-08-01 07:21:28.656 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 422/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.9, lr: 4.175e-04, size: 352, ETA: 1:06:26
2025-08-01 07:21:31.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 422/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 4.168e-04, size: 416, ETA: 1:06:22
2025-08-01 07:21:33.530 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:21:40.310 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:21:40.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:21:41.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4517
2025-08-01 07:21:41.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4489
2025-08-01 07:21:41.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2115
2025-08-01 07:21:41.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3707
2025-08-01 07:21:41.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:21:41.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:21:41.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 07:21:41.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-01 07:21:41.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-01 07:21:41.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:21:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:21:41.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:21:42.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:21:42.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:21:42.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:21:43.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:21:43.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:21:44.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:21:44.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:21:44.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:21:44.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:21:44.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 07:21:44.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:21:44.871 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.82 ms, Average inference time: 8.35 ms

2025-08-01 07:21:44.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:21:44.955 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:21:45.034 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch423
2025-08-01 07:21:48.488 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 423/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 4.159e-04, size: 576, ETA: 1:06:17
2025-08-01 07:21:51.988 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 423/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.8, lr: 4.152e-04, size: 256, ETA: 1:06:14
2025-08-01 07:21:55.181 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 423/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 0.8, lr: 4.145e-04, size: 320, ETA: 1:06:10
2025-08-01 07:21:58.437 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 423/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 4.139e-04, size: 320, ETA: 1:06:06
2025-08-01 07:22:01.743 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 423/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, 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: 4.132e-04, size: 480, ETA: 1:06:03
2025-08-01 07:22:04.946 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 423/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 4.125e-04, size: 288, ETA: 1:05:59
2025-08-01 07:22:06.348 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:22:13.122 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:22:14.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:22:14.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5040
2025-08-01 07:22:14.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4285
2025-08-01 07:22:14.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3025
2025-08-01 07:22:14.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4117
2025-08-01 07:22:14.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:22:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:22:14.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:22:14.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:22:14.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:22:15.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:22:16.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:22:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:22:17.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:22:18.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:22:19.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:22:20.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:22:21.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:22:21.744 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:22:21.745 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 07:22:21.745 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 07:22:21.745 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:22:21.752 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.86 ms, Average inference time: 8.29 ms

2025-08-01 07:22:21.754 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:22:21.834 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:22:21.918 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch424
2025-08-01 07:22:25.185 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 424/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.8, lr: 4.115e-04, size: 320, ETA: 1:05:54
2025-08-01 07:22:28.683 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 424/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.7, lr: 4.109e-04, size: 576, ETA: 1:05:51
2025-08-01 07:22:32.048 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 424/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 4.102e-04, size: 480, ETA: 1:05:47
2025-08-01 07:22:35.427 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 424/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 4.095e-04, size: 512, ETA: 1:05:43
2025-08-01 07:22:38.873 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 424/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 4.089e-04, size: 320, ETA: 1:05:40
2025-08-01 07:22:42.272 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 424/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 4.082e-04, size: 288, ETA: 1:05:36
2025-08-01 07:22:43.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:22:50.478 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:22:51.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:22:51.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4414
2025-08-01 07:22:52.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3924
2025-08-01 07:22:52.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2226
2025-08-01 07:22:52.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3521
2025-08-01 07:22:52.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:22:52.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:22:52.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-08-01 07:22:52.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:22:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:22:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:22:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:22:52.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:22:53.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:22:54.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:22:55.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:22:55.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:22:56.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:22:57.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:22:57.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:22:58.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:22:58.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:22:58.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 07:22:58.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:22:58.637 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.84 ms, Average inference time: 8.35 ms

2025-08-01 07:22:58.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:22:58.714 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:22:58.820 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch425
2025-08-01 07:23:02.187 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 425/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 4.073e-04, size: 352, ETA: 1:05:31
2025-08-01 07:23:05.655 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 425/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 9.7, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 4.7, cls_loss: 0.7, lr: 4.066e-04, size: 480, ETA: 1:05:28
2025-08-01 07:23:09.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 425/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 2.4, cls_loss: 0.7, lr: 4.059e-04, size: 480, ETA: 1:05:24
2025-08-01 07:23:12.399 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 425/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, 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: 4.053e-04, size: 384, ETA: 1:05:21
2025-08-01 07:23:15.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 425/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.9, lr: 4.046e-04, size: 288, ETA: 1:05:17
2025-08-01 07:23:18.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 425/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 4.039e-04, size: 352, ETA: 1:05:13
2025-08-01 07:23:20.261 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:23:27.138 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:23:28.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:23:28.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4628
2025-08-01 07:23:28.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3637
2025-08-01 07:23:28.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2243
2025-08-01 07:23:28.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3503
2025-08-01 07:23:28.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:23:28.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.350
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:23:28.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:23:28.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:23:28.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:23:29.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:23:30.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:23:30.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:23:31.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:23:32.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:23:32.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:23:33.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:23:34.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:23:34.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:23:34.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 07:23:34.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 07:23:34.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:23:34.990 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.86 ms, Average inference time: 8.36 ms

2025-08-01 07:23:34.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:23:35.117 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:23:35.261 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch426
2025-08-01 07:23:38.244 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 426/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 4.030e-04, size: 352, ETA: 1:05:08
2025-08-01 07:23:41.578 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 426/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 4.023e-04, size: 480, ETA: 1:05:05
2025-08-01 07:23:44.878 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 426/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 4.016e-04, size: 256, ETA: 1:05:01
2025-08-01 07:23:48.213 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 426/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.2, cls_loss: 0.6, lr: 4.010e-04, size: 512, ETA: 1:04:57
2025-08-01 07:23:51.396 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 426/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 4.003e-04, size: 480, ETA: 1:04:54
2025-08-01 07:23:54.854 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 426/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 3.997e-04, size: 384, ETA: 1:04:50
2025-08-01 07:23:56.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:24:03.189 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:24:04.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:24:04.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4997
2025-08-01 07:24:04.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4246
2025-08-01 07:24:04.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3097
2025-08-01 07:24:04.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4114
2025-08-01 07:24:04.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:24:04.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.411
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:24:04.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:24:04.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:24:05.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:24:06.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:24:06.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:24:07.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:24:08.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:24:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:24:09.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:24:09.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:24:10.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:24:10.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:24:10.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 07:24:10.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:24:10.572 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.84 ms, Average inference time: 8.23 ms

2025-08-01 07:24:10.573 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:24:10.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:24:10.735 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch427
2025-08-01 07:24:13.962 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 427/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 9.7, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 4.7, cls_loss: 0.7, lr: 3.987e-04, size: 256, ETA: 1:04:45
2025-08-01 07:24:17.213 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 427/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 3.980e-04, size: 320, ETA: 1:04:42
2025-08-01 07:24:20.588 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 427/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 3.974e-04, size: 544, ETA: 1:04:38
2025-08-01 07:24:23.957 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 427/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, 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: 3.967e-04, size: 480, ETA: 1:04:35
2025-08-01 07:24:27.497 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 427/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 3.961e-04, size: 416, ETA: 1:04:31
2025-08-01 07:24:30.909 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 427/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.6, lr: 3.954e-04, size: 544, ETA: 1:04:28
2025-08-01 07:24:32.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:24:39.340 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:24:40.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:24:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4440
2025-08-01 07:24:40.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4112
2025-08-01 07:24:40.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1961
2025-08-01 07:24:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3504
2025-08-01 07:24:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:24:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:24:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 07:24:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-01 07:24:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.196
2025-08-01 07:24:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.350
2025-08-01 07:24:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:24:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:24:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:24:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:24:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:24:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:24:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:24:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:24:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:24:41.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:24:41.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:24:42.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:24:42.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:24:43.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:24:43.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:24:44.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:24:44.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:24:45.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:24:45.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:24:45.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 07:24:45.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:24:45.406 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.85 ms, Average inference time: 8.18 ms

2025-08-01 07:24:45.407 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:24:45.490 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:24:45.569 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch428
2025-08-01 07:24:48.834 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 428/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.7, lr: 3.945e-04, size: 288, ETA: 1:04:22
2025-08-01 07:24:52.248 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 428/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.7, lr: 3.938e-04, size: 544, ETA: 1:04:19
2025-08-01 07:24:55.681 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 428/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, 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: 3.931e-04, size: 288, ETA: 1:04:15
2025-08-01 07:24:58.855 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 428/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 3.925e-04, size: 544, ETA: 1:04:12
2025-08-01 07:25:02.335 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 428/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 1.2, lr: 3.918e-04, size: 256, ETA: 1:04:08
2025-08-01 07:25:05.678 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 428/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 3.912e-04, size: 512, ETA: 1:04:05
2025-08-01 07:25:07.232 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:25:14.017 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:25:14.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:25:15.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4987
2025-08-01 07:25:15.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3948
2025-08-01 07:25:15.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2660
2025-08-01 07:25:15.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3865
2025-08-01 07:25:15.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:25:15.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:25:15.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-01 07:25:15.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-01 07:25:15.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-08-01 07:25:15.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-08-01 07:25:15.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:25:15.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:25:15.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:25:15.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:25:15.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:25:15.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:25:15.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:25:15.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:25:15.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:25:16.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:25:16.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:25:17.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:25:18.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:25:18.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:25:19.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:25:20.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:25:20.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:25:21.216 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:25:21.217 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:25:21.217 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:25:21.217 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:25:21.226 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.82 ms, Average inference time: 8.25 ms

2025-08-01 07:25:21.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:25:21.347 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:25:21.460 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch429
2025-08-01 07:25:24.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 429/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 3.902e-04, size: 256, ETA: 1:04:00
2025-08-01 07:25:27.980 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 429/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 3.896e-04, size: 288, ETA: 1:03:56
2025-08-01 07:25:31.260 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 429/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.8, lr: 3.889e-04, size: 480, ETA: 1:03:52
2025-08-01 07:25:34.600 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 429/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 3.883e-04, size: 320, ETA: 1:03:49
2025-08-01 07:25:37.852 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 429/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.4, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 3.876e-04, size: 352, ETA: 1:03:45
2025-08-01 07:25:41.222 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 429/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 3.870e-04, size: 576, ETA: 1:03:42
2025-08-01 07:25:42.959 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:25:49.771 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:25:50.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:25:50.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4620
2025-08-01 07:25:51.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3867
2025-08-01 07:25:51.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1861
2025-08-01 07:25:51.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3449
2025-08-01 07:25:51.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:25:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:25:51.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:25:51.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:25:51.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:25:51.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:25:52.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:25:52.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:25:53.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:25:53.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:25:54.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:25:55.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:25:55.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:25:56.188 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:25:56.188 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 07:25:56.188 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 07:25:56.188 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:25:56.196 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.86 ms, Average inference time: 8.21 ms

2025-08-01 07:25:56.197 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:25:56.324 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:25:56.405 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch430
2025-08-01 07:25:59.693 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 430/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.8, lr: 3.860e-04, size: 448, ETA: 1:03:37
2025-08-01 07:26:03.180 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 430/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, 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: 3.854e-04, size: 320, ETA: 1:03:33
2025-08-01 07:26:06.766 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 430/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 3.847e-04, size: 544, ETA: 1:03:30
2025-08-01 07:26:10.413 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 430/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.7, lr: 3.841e-04, size: 480, ETA: 1:03:26
2025-08-01 07:26:13.919 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 430/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 3.834e-04, size: 512, ETA: 1:03:23
2025-08-01 07:26:17.423 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 430/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 3.828e-04, size: 448, ETA: 1:03:19
2025-08-01 07:26:18.972 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:26:25.713 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:26:26.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:26:26.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4898
2025-08-01 07:26:26.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4222
2025-08-01 07:26:27.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2764
2025-08-01 07:26:27.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3961
2025-08-01 07:26:27.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:26:27.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:26:27.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-01 07:26:27.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-08-01 07:26:27.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-08-01 07:26:27.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-08-01 07:26:27.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:26:27.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:26:27.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:26:27.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:26:27.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:26:27.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:26:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:26:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:26:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:26:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:26:28.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:26:28.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:26:29.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:26:29.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:26:30.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:26:31.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:26:31.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:26:32.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:26:32.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 07:26:32.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 07:26:32.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:26:32.242 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.86 ms, Average inference time: 8.25 ms

2025-08-01 07:26:32.244 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:26:32.322 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:26:32.402 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch431
2025-08-01 07:26:35.675 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 431/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 3.818e-04, size: 352, ETA: 1:03:14
2025-08-01 07:26:38.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 431/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 1.5, cls_loss: 0.6, lr: 3.812e-04, size: 256, ETA: 1:03:11
2025-08-01 07:26:42.399 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 431/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, 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: 3.805e-04, size: 544, ETA: 1:03:07
2025-08-01 07:26:45.762 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 431/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.6, lr: 3.799e-04, size: 480, ETA: 1:03:04
2025-08-01 07:26:49.001 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 431/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.6, lr: 3.792e-04, size: 256, ETA: 1:03:00
2025-08-01 07:26:52.445 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 431/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 3.786e-04, size: 576, ETA: 1:02:57
2025-08-01 07:26:53.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:27:00.695 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:27:01.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:27:01.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4202
2025-08-01 07:27:01.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4004
2025-08-01 07:27:02.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1906
2025-08-01 07:27:02.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3371
2025-08-01 07:27:02.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.420
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:27:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:27:02.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:27:02.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:27:02.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:27:02.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:27:02.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:27:02.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:27:03.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:27:03.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:27:04.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:27:05.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:27:05.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:27:06.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:27:06.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:27:07.328 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:27:07.328 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:27:07.328 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 07:27:07.328 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:27:07.335 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.82 ms, Average inference time: 8.34 ms

2025-08-01 07:27:07.336 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:27:07.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:27:07.534 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch432
2025-08-01 07:27:10.828 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 432/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 3.776e-04, size: 384, ETA: 1:02:52
2025-08-01 07:27:14.302 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 432/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 3.770e-04, size: 288, ETA: 1:02:48
2025-08-01 07:27:17.911 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 432/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 3.764e-04, size: 512, ETA: 1:02:45
2025-08-01 07:27:21.512 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 432/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.9, lr: 3.757e-04, size: 576, ETA: 1:02:41
2025-08-01 07:27:25.167 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 432/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 1.0, lr: 3.751e-04, size: 448, ETA: 1:02:38
2025-08-01 07:27:28.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 432/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 3.744e-04, size: 352, ETA: 1:02:34
2025-08-01 07:27:29.948 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:27:36.658 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:27:37.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:27:37.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4758
2025-08-01 07:27:37.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3862
2025-08-01 07:27:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2967
2025-08-01 07:27:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3862
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.386
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:27:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:27:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:27:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:27:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:27:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:27:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:27:38.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:27:39.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:27:39.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:27:40.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:27:40.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:27:41.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:27:41.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:27:42.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:27:42.996 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:27:42.997 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:27:42.997 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:27:42.997 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:27:43.004 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.87 ms, Average inference time: 8.33 ms

2025-08-01 07:27:43.005 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:27:43.090 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:27:43.172 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch433
2025-08-01 07:27:46.328 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 433/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 3.735e-04, size: 416, ETA: 1:02:29
2025-08-01 07:27:49.820 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 433/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.7, lr: 3.728e-04, size: 320, ETA: 1:02:26
2025-08-01 07:27:52.994 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 433/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, 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: 3.722e-04, size: 288, ETA: 1:02:22
2025-08-01 07:27:56.182 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 433/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 3.2, cls_loss: 0.5, lr: 3.716e-04, size: 416, ETA: 1:02:18
2025-08-01 07:27:59.585 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 433/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 1.1, lr: 3.709e-04, size: 288, ETA: 1:02:15
2025-08-01 07:28:03.030 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 433/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 3.703e-04, size: 544, ETA: 1:02:11
2025-08-01 07:28:04.507 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:28:11.425 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:28:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:28:12.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3806
2025-08-01 07:28:12.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2025
2025-08-01 07:28:12.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1352
2025-08-01 07:28:12.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2394
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.135
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.239
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:28:12.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:28:12.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:28:12.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:28:12.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:28:12.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:28:12.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:28:12.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:28:13.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:28:13.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:28:14.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:28:14.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:28:15.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:28:15.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:28:16.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:28:16.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:28:17.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:28:17.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 07:28:17.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 07:28:17.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:28:17.203 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.83 ms, Average inference time: 8.30 ms

2025-08-01 07:28:17.207 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:28:17.338 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:28:17.422 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch434
2025-08-01 07:28:20.816 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 434/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.5, lr: 3.693e-04, size: 544, ETA: 1:02:06
2025-08-01 07:28:24.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 434/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 1.0, lr: 3.687e-04, size: 576, ETA: 1:02:03
2025-08-01 07:28:27.760 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 434/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 8.8, iou_loss: 2.5, l1_loss: 2.2, conf_loss: 3.6, cls_loss: 0.6, lr: 3.681e-04, size: 576, ETA: 1:01:59
2025-08-01 07:28:31.370 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 434/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 3.674e-04, size: 576, ETA: 1:01:56
2025-08-01 07:28:34.886 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 434/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, 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: 3.668e-04, size: 480, ETA: 1:01:52
2025-08-01 07:28:38.241 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 434/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.8, lr: 3.662e-04, size: 256, ETA: 1:01:49
2025-08-01 07:28:39.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:28:46.375 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:28:46.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:28:47.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3920
2025-08-01 07:28:47.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3903
2025-08-01 07:28:47.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1691
2025-08-01 07:28:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3171
2025-08-01 07:28:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:28:47.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:28:47.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 07:28:47.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-01 07:28:47.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-08-01 07:28:47.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.317
2025-08-01 07:28:47.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:28:47.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:28:47.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:28:47.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:28:47.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:28:47.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:28:47.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:28:47.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:28:47.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:28:47.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:28:47.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:28:48.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:28:48.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:28:48.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:28:49.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:28:49.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:28:49.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:28:50.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:28:50.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:28:50.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 07:28:50.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:28:50.007 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.74 ms, Average inference time: 8.20 ms

2025-08-01 07:28:50.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:28:50.156 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:28:50.240 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch435
2025-08-01 07:28:53.533 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 435/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.6, lr: 3.652e-04, size: 576, ETA: 1:01:44
2025-08-01 07:28:57.078 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 435/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 3.646e-04, size: 512, ETA: 1:01:40
2025-08-01 07:29:00.361 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 435/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 3.640e-04, size: 320, ETA: 1:01:37
2025-08-01 07:29:03.655 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 435/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.4, cls_loss: 0.9, lr: 3.633e-04, size: 512, ETA: 1:01:33
2025-08-01 07:29:06.992 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 435/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, 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: 3.627e-04, size: 352, ETA: 1:01:29
2025-08-01 07:29:10.454 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 435/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 3.620e-04, size: 448, ETA: 1:01:26
2025-08-01 07:29:11.995 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:29:18.971 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:29:20.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:29:21.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4777
2025-08-01 07:29:21.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4197
2025-08-01 07:29:21.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2492
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3822
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.420
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:29:21.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:29:21.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:29:21.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:29:21.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:29:21.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:29:21.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:29:21.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:29:22.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:29:23.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:29:24.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:29:25.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:29:27.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:29:28.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:29:29.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:29:30.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:29:31.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:29:31.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:29:31.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:29:31.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:29:31.639 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.87 ms, Average inference time: 8.48 ms

2025-08-01 07:29:31.641 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:29:31.753 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:29:31.836 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch436
2025-08-01 07:29:34.924 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 436/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 3.611e-04, size: 320, ETA: 1:01:21
2025-08-01 07:29:38.247 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 436/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 1.0, lr: 3.605e-04, size: 352, ETA: 1:01:17
2025-08-01 07:29:41.505 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 436/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 3.6, cls_loss: 0.5, lr: 3.599e-04, size: 288, ETA: 1:01:14
2025-08-01 07:29:44.851 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 436/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.5, lr: 3.592e-04, size: 576, ETA: 1:01:10
2025-08-01 07:29:48.466 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 436/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 3.586e-04, size: 256, ETA: 1:01:07
2025-08-01 07:29:51.886 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 436/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 3.580e-04, size: 256, ETA: 1:01:03
2025-08-01 07:29:53.346 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:30:00.153 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:30:01.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:30:01.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4836
2025-08-01 07:30:01.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4294
2025-08-01 07:30:01.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2658
2025-08-01 07:30:01.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3929
2025-08-01 07:30:01.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.393
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:30:01.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:30:01.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:30:01.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:30:01.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:30:02.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:30:03.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:30:04.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:30:05.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:30:05.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:30:06.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:30:07.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:30:08.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:30:08.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:30:08.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:30:08.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:30:08.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:30:08.987 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.86 ms, Average inference time: 8.33 ms

2025-08-01 07:30:08.991 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:30:09.074 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:30:09.158 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch437
2025-08-01 07:30:12.289 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 437/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 3.570e-04, size: 416, ETA: 1:00:58
2025-08-01 07:30:15.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 437/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 3.564e-04, size: 288, ETA: 1:00:54
2025-08-01 07:30:18.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 437/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 3.558e-04, size: 384, ETA: 1:00:51
2025-08-01 07:30:21.934 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 437/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 3.551e-04, size: 320, ETA: 1:00:47
2025-08-01 07:30:25.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 437/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 3.545e-04, size: 384, ETA: 1:00:44
2025-08-01 07:30:28.463 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 437/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 3.539e-04, size: 448, ETA: 1:00:40
2025-08-01 07:30:30.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:30:36.769 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:30:37.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:30:38.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5309
2025-08-01 07:30:38.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4755
2025-08-01 07:30:38.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2765
2025-08-01 07:30:38.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4276
2025-08-01 07:30:38.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:30:38.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:30:38.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:30:38.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:30:38.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:30:38.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:30:38.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:30:39.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:30:40.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:30:40.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:30:41.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:30:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:30:42.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:30:42.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:30:43.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:30:43.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 07:30:43.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 07:30:43.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:30:43.317 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.81 ms, Average inference time: 8.34 ms

2025-08-01 07:30:43.318 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:30:43.395 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:30:43.476 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch438
2025-08-01 07:30:46.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 438/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.8, lr: 3.530e-04, size: 576, ETA: 1:00:35
2025-08-01 07:30:50.263 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 438/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 3.523e-04, size: 320, ETA: 1:00:31
2025-08-01 07:30:53.492 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 438/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.9, lr: 3.517e-04, size: 256, ETA: 1:00:28
2025-08-01 07:30:57.048 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 438/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 3.511e-04, size: 352, ETA: 1:00:24
2025-08-01 07:31:00.159 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 438/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 2.7, cls_loss: 0.9, lr: 3.504e-04, size: 384, ETA: 1:00:21
2025-08-01 07:31:03.421 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 438/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 3.498e-04, size: 544, ETA: 1:00:17
2025-08-01 07:31:04.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:31:11.746 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:31:12.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:31:12.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3801
2025-08-01 07:31:12.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3588
2025-08-01 07:31:12.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1388
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2926
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.139
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.293
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:31:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:31:12.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:31:12.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:31:12.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:31:12.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:31:12.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:31:12.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:31:13.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:31:13.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:31:14.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:31:15.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:31:15.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:31:16.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:31:16.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:31:17.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:31:17.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:31:17.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 07:31:17.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 07:31:17.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:31:17.698 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.83 ms, Average inference time: 8.30 ms

2025-08-01 07:31:17.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:31:17.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:31:17.867 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch439
2025-08-01 07:31:20.933 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 439/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.9, lr: 3.489e-04, size: 416, ETA: 1:00:12
2025-08-01 07:31:24.313 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 439/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.7, lr: 3.483e-04, size: 256, ETA: 1:00:09
2025-08-01 07:31:27.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 439/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.0, cls_loss: 0.6, lr: 3.477e-04, size: 256, ETA: 1:00:05
2025-08-01 07:31:31.154 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 439/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 3.470e-04, size: 416, ETA: 1:00:02
2025-08-01 07:31:34.351 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 439/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 3.464e-04, size: 384, ETA: 0:59:58
2025-08-01 07:31:37.607 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 439/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 1.6, cls_loss: 0.6, lr: 3.458e-04, size: 544, ETA: 0:59:54
2025-08-01 07:31:39.161 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:31:45.811 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:31:46.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:31:46.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4506
2025-08-01 07:31:46.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4188
2025-08-01 07:31:46.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2257
2025-08-01 07:31:46.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3650
2025-08-01 07:31:46.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:31:46.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:31:46.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 07:31:46.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.226
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.365
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:31:46.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:31:46.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:31:46.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:31:47.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:31:47.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:31:48.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:31:48.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:31:48.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:31:49.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:31:49.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:31:50.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:31:50.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:31:50.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:31:50.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 07:31:50.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:31:50.420 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.83 ms, Average inference time: 8.32 ms

2025-08-01 07:31:50.422 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:31:50.516 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:31:50.618 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch440
2025-08-01 07:31:54.042 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 440/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 3.449e-04, size: 288, ETA: 0:59:49
2025-08-01 07:31:57.516 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 440/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.9, lr: 3.443e-04, size: 576, ETA: 0:59:46
2025-08-01 07:32:00.895 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 440/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 3.436e-04, size: 416, ETA: 0:59:42
2025-08-01 07:32:04.371 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 440/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 3.430e-04, size: 256, ETA: 0:59:39
2025-08-01 07:32:07.675 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 440/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 3.424e-04, size: 416, ETA: 0:59:35
2025-08-01 07:32:11.054 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 440/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 3.418e-04, size: 544, ETA: 0:59:32
2025-08-01 07:32:12.597 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:32:19.246 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:32:19.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:32:19.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4168
2025-08-01 07:32:20.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3840
2025-08-01 07:32:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2200
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3402
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.220
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.340
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:32:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:32:20.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:32:20.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:32:20.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:32:20.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:32:20.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:32:20.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:32:20.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:32:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:32:21.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:32:21.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:32:21.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:32:22.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:32:22.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:32:22.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:32:23.164 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:32:23.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 07:32:23.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 07:32:23.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:32:23.173 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.79 ms, Average inference time: 8.19 ms

2025-08-01 07:32:23.175 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:32:23.262 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:32:23.341 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch441
2025-08-01 07:32:26.512 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 441/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 3.409e-04, size: 288, ETA: 0:59:27
2025-08-01 07:32:29.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 441/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 3.402e-04, size: 544, ETA: 0:59:23
2025-08-01 07:32:32.997 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 441/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.8, lr: 3.396e-04, size: 352, ETA: 0:59:20
2025-08-01 07:32:36.405 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 441/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 3.390e-04, size: 288, ETA: 0:59:16
2025-08-01 07:32:39.811 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 441/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, 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: 3.384e-04, size: 544, ETA: 0:59:13
2025-08-01 07:32:43.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 441/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 3.378e-04, size: 320, ETA: 0:59:09
2025-08-01 07:32:44.581 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:32:51.493 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:32:52.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:32:52.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4704
2025-08-01 07:32:52.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4322
2025-08-01 07:32:52.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2358
2025-08-01 07:32:52.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3794
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.379
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:32:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:32:52.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:32:52.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:32:52.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:32:52.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:32:52.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:32:52.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:32:53.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:32:53.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:32:54.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:32:54.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:32:55.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:32:55.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:32:56.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:32:56.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:32:57.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:32:57.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:32:57.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:32:57.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:32:57.487 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.68 ms, Average NMS time: 0.87 ms, Average inference time: 8.55 ms

2025-08-01 07:32:57.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:32:57.565 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:32:57.663 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch442
2025-08-01 07:33:01.025 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 442/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 3.369e-04, size: 352, ETA: 0:59:04
2025-08-01 07:33:04.220 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 442/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, 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: 3.363e-04, size: 288, ETA: 0:59:00
2025-08-01 07:33:07.955 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 442/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.182s, 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: 3.356e-04, size: 512, ETA: 0:58:57
2025-08-01 07:33:11.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 442/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 3.350e-04, size: 384, ETA: 0:58:53
2025-08-01 07:33:14.472 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 442/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 1.1, lr: 3.344e-04, size: 288, ETA: 0:58:50
2025-08-01 07:33:17.609 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 442/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 3.338e-04, size: 256, ETA: 0:58:46
2025-08-01 07:33:19.199 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:33:26.150 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:33:26.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:33:27.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4768
2025-08-01 07:33:27.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3875
2025-08-01 07:33:27.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2754
2025-08-01 07:33:27.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3799
2025-08-01 07:33:27.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:33:27.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:33:27.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:33:27.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:33:27.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:33:27.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:33:27.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:33:28.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:33:28.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:33:29.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:33:30.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:33:30.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:33:31.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:33:31.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:33:32.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:33:33.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:33:33.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:33:33.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:33:33.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:33:33.126 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.88 ms, Average inference time: 8.46 ms

2025-08-01 07:33:33.127 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:33:33.259 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:33:33.341 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch443
2025-08-01 07:33:36.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 443/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.8, lr: 3.329e-04, size: 256, ETA: 0:58:41
2025-08-01 07:33:39.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 443/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 3.323e-04, size: 416, ETA: 0:58:38
2025-08-01 07:33:43.202 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 443/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.9, lr: 3.317e-04, size: 512, ETA: 0:58:34
2025-08-01 07:33:46.427 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 443/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 3.311e-04, size: 384, ETA: 0:58:30
2025-08-01 07:33:49.865 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 443/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 3.304e-04, size: 512, ETA: 0:58:27
2025-08-01 07:33:53.156 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 443/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.8, lr: 3.298e-04, size: 448, ETA: 0:58:23
2025-08-01 07:33:54.740 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:34:01.482 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:34:02.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:34:02.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4995
2025-08-01 07:34:02.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4388
2025-08-01 07:34:02.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2714
2025-08-01 07:34:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4032
2025-08-01 07:34:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:34:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:34:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-01 07:34:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 07:34:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-08-01 07:34:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.403
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:34:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:34:03.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:34:03.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:34:04.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:34:04.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:34:05.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:34:05.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:34:06.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:34:06.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:34:07.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:34:07.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 07:34:07.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 07:34:07.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:34:07.112 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.81 ms, Average inference time: 8.25 ms

2025-08-01 07:34:07.113 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:34:07.196 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:34:07.276 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch444
2025-08-01 07:34:10.418 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 444/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 3.289e-04, size: 512, ETA: 0:58:18
2025-08-01 07:34:13.749 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 444/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, 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: 3.283e-04, size: 416, ETA: 0:58:15
2025-08-01 07:34:17.103 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 444/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 3.277e-04, size: 480, ETA: 0:58:11
2025-08-01 07:34:20.524 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 444/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.7, lr: 3.271e-04, size: 416, ETA: 0:58:08
2025-08-01 07:34:24.017 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 444/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 3.265e-04, size: 256, ETA: 0:58:04
2025-08-01 07:34:27.579 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 444/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, 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: 3.259e-04, size: 352, ETA: 0:58:01
2025-08-01 07:34:29.097 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:34:35.937 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:34:36.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:34:37.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4465
2025-08-01 07:34:37.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4167
2025-08-01 07:34:37.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2633
2025-08-01 07:34:37.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3755
2025-08-01 07:34:37.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.375
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:34:37.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:34:37.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:34:37.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:34:37.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:34:38.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:34:39.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:34:40.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:34:40.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:34:41.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:34:42.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:34:43.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:34:43.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:34:44.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:34:44.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:34:44.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:34:44.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:34:44.638 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.93 ms, Average inference time: 8.34 ms

2025-08-01 07:34:44.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:34:44.720 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:34:44.805 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch445
2025-08-01 07:34:48.053 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 445/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.7, lr: 3.250e-04, size: 448, ETA: 0:57:56
2025-08-01 07:34:51.529 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 445/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 1.2, cls_loss: 0.6, lr: 3.244e-04, size: 544, ETA: 0:57:52
2025-08-01 07:34:54.859 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 445/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 3.238e-04, size: 544, ETA: 0:57:49
2025-08-01 07:34:58.186 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 445/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.2, iou_loss: 3.3, l1_loss: 1.4, conf_loss: 2.7, cls_loss: 0.8, lr: 3.232e-04, size: 416, ETA: 0:57:45
2025-08-01 07:35:01.337 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 445/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 3.226e-04, size: 288, ETA: 0:57:42
2025-08-01 07:35:04.939 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 445/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.179s, 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: 3.220e-04, size: 416, ETA: 0:57:38
2025-08-01 07:35:06.398 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:35:13.196 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:35:13.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:35:14.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4805
2025-08-01 07:35:14.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4282
2025-08-01 07:35:14.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3024
2025-08-01 07:35:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4037
2025-08-01 07:35:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:35:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:35:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 07:35:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.404
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:35:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:35:14.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:35:15.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:35:15.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:35:16.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:35:16.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:35:17.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:35:17.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:35:18.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:35:19.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:35:19.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:35:19.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:35:19.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 07:35:19.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:35:19.694 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.84 ms, Average inference time: 8.23 ms

2025-08-01 07:35:19.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:35:19.777 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:35:19.861 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch446
2025-08-01 07:35:23.143 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 446/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.7, lr: 3.211e-04, size: 448, ETA: 0:57:33
2025-08-01 07:35:26.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 446/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 3.205e-04, size: 416, ETA: 0:57:29
2025-08-01 07:35:29.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 446/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.8, lr: 3.199e-04, size: 448, ETA: 0:57:26
2025-08-01 07:35:33.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 446/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 3.193e-04, size: 416, ETA: 0:57:22
2025-08-01 07:35:36.566 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 446/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 3.187e-04, size: 448, ETA: 0:57:19
2025-08-01 07:35:39.932 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 446/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 3.181e-04, size: 480, ETA: 0:57:15
2025-08-01 07:35:41.477 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:35:48.381 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:35:48.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:35:49.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3984
2025-08-01 07:35:49.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4435
2025-08-01 07:35:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1845
2025-08-01 07:35:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3421
2025-08-01 07:35:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:35:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:35:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-08-01 07:35:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.185
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.342
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:35:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:35:49.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:35:49.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:35:49.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:35:50.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:35:50.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:35:50.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:35:51.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:35:51.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:35:51.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:35:52.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:35:52.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:35:52.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 07:35:52.119 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:35:52.126 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.75 ms, Average inference time: 8.31 ms

2025-08-01 07:35:52.126 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:35:52.229 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:35:52.319 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch447
2025-08-01 07:35:55.756 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 447/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 3.172e-04, size: 384, ETA: 0:57:10
2025-08-01 07:35:59.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 447/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.9, lr: 3.166e-04, size: 256, ETA: 0:57:07
2025-08-01 07:36:02.553 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 447/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, 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: 3.160e-04, size: 352, ETA: 0:57:03
2025-08-01 07:36:06.110 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 447/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 3.154e-04, size: 320, ETA: 0:57:00
2025-08-01 07:36:09.504 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 447/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 3.148e-04, size: 320, ETA: 0:56:56
2025-08-01 07:36:12.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 447/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 3.142e-04, size: 416, ETA: 0:56:53
2025-08-01 07:36:14.232 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:36:21.151 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:36:21.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:36:22.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4851
2025-08-01 07:36:22.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4161
2025-08-01 07:36:22.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2584
2025-08-01 07:36:22.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3865
2025-08-01 07:36:22.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:36:22.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:36:22.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-08-01 07:36:22.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 07:36:22.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 07:36:22.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-08-01 07:36:22.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:36:22.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:36:22.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:36:22.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:36:22.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:36:22.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:36:22.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:36:22.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:36:22.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:36:22.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:36:23.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:36:23.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:36:23.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:36:24.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:36:24.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:36:25.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:36:25.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:36:25.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:36:25.823 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 07:36:25.823 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:36:25.823 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:36:25.830 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.83 ms, Average inference time: 8.39 ms

2025-08-01 07:36:25.831 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:36:25.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:36:25.994 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch448
2025-08-01 07:36:29.337 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 448/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 3.133e-04, size: 256, ETA: 0:56:48
2025-08-01 07:36:32.685 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 448/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 3.127e-04, size: 576, ETA: 0:56:44
2025-08-01 07:36:36.138 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 448/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 3.121e-04, size: 512, ETA: 0:56:41
2025-08-01 07:36:39.537 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 448/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, 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: 3.115e-04, size: 256, ETA: 0:56:37
2025-08-01 07:36:42.911 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 448/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 10.4, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 10.4, cls_loss: 0.0, lr: 3.109e-04, size: 480, ETA: 0:56:34
2025-08-01 07:36:46.453 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 448/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 3.103e-04, size: 448, ETA: 0:56:30
2025-08-01 07:36:47.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:36:54.831 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:36:55.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:36:55.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4678
2025-08-01 07:36:55.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4124
2025-08-01 07:36:55.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2267
2025-08-01 07:36:55.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3690
2025-08-01 07:36:55.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:36:55.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:36:55.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-01 07:36:55.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-01 07:36:55.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:36:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:36:55.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:36:56.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:36:57.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:36:57.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:36:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:36:58.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:36:59.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:36:59.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:37:00.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:37:00.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:37:00.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:37:00.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 07:37:00.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:37:00.638 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.83 ms, Average inference time: 8.26 ms

2025-08-01 07:37:00.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:37:00.759 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:37:00.910 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch449
2025-08-01 07:37:04.308 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 449/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 3.094e-04, size: 544, ETA: 0:56:25
2025-08-01 07:37:07.591 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 449/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.3, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.9, lr: 3.088e-04, size: 384, ETA: 0:56:22
2025-08-01 07:37:10.848 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 449/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 3.082e-04, size: 416, ETA: 0:56:18
2025-08-01 07:37:14.220 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 449/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 3.077e-04, size: 320, ETA: 0:56:14
2025-08-01 07:37:17.530 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 449/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 3.071e-04, size: 448, ETA: 0:56:11
2025-08-01 07:37:20.830 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 449/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 1.0, lr: 3.065e-04, size: 288, ETA: 0:56:07
2025-08-01 07:37:22.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:37:29.267 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:37:30.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:37:30.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4755
2025-08-01 07:37:30.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4551
2025-08-01 07:37:30.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2778
2025-08-01 07:37:30.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4028
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.403
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:37:30.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:37:30.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:37:30.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:37:30.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:37:30.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:37:30.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:37:31.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:37:31.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:37:32.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:37:33.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:37:34.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:37:34.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:37:35.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:37:36.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:37:36.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:37:36.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 07:37:36.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 07:37:36.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:37:36.862 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.68 ms, Average NMS time: 0.86 ms, Average inference time: 8.54 ms

2025-08-01 07:37:36.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:37:37.012 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:37:37.118 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch450
2025-08-01 07:37:40.281 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 450/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 3.056e-04, size: 288, ETA: 0:56:02
2025-08-01 07:37:43.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 450/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.9, lr: 3.050e-04, size: 448, ETA: 0:55:59
2025-08-01 07:37:47.047 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 450/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 3.044e-04, size: 320, ETA: 0:55:55
2025-08-01 07:37:50.572 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 450/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.8, lr: 3.038e-04, size: 256, ETA: 0:55:52
2025-08-01 07:37:53.789 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 450/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 3.032e-04, size: 256, ETA: 0:55:48
2025-08-01 07:37:57.168 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 450/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, 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: 3.026e-04, size: 448, ETA: 0:55:45
2025-08-01 07:37:58.610 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:38:05.362 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:38:06.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:38:06.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5250
2025-08-01 07:38:07.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4439
2025-08-01 07:38:07.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3461
2025-08-01 07:38:07.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4383
2025-08-01 07:38:07.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:38:07.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:38:07.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-01 07:38:07.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.346
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:38:07.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:38:07.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:38:07.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:38:08.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:38:09.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:38:10.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:38:11.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:38:11.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:38:12.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:38:13.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:38:14.194 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:38:14.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-01 07:38:14.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-01 07:38:14.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:38:14.202 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.88 ms, Average inference time: 8.30 ms

2025-08-01 07:38:14.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:38:14.331 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:38:14.413 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch451
2025-08-01 07:38:17.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 451/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 3.018e-04, size: 384, ETA: 0:55:40
2025-08-01 07:38:20.972 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 451/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 3.012e-04, size: 512, ETA: 0:55:36
2025-08-01 07:38:24.343 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 451/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 3.006e-04, size: 512, ETA: 0:55:33
2025-08-01 07:38:27.536 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 451/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 3.000e-04, size: 448, ETA: 0:55:29
2025-08-01 07:38:30.846 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 451/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 2.994e-04, size: 352, ETA: 0:55:26
2025-08-01 07:38:34.246 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 451/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, 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: 2.988e-04, size: 384, ETA: 0:55:22
2025-08-01 07:38:35.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:38:42.592 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:38:43.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:38:44.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4793
2025-08-01 07:38:44.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4566
2025-08-01 07:38:44.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2319
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3892
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.389
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:38:44.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:38:44.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:38:44.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:38:44.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:38:44.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:38:44.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:38:44.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:38:44.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:38:45.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:38:46.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:38:47.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:38:48.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:38:49.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:38:50.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:38:51.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:38:51.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:38:52.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:38:52.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:38:52.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:38:52.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:38:52.848 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.88 ms, Average inference time: 8.40 ms

2025-08-01 07:38:52.849 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:38:52.980 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:38:53.053 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch452
2025-08-01 07:38:56.167 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 452/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.9, lr: 2.980e-04, size: 448, ETA: 0:55:17
2025-08-01 07:38:59.381 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 452/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 2.974e-04, size: 352, ETA: 0:55:13
2025-08-01 07:39:02.925 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 452/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 2.968e-04, size: 512, ETA: 0:55:10
2025-08-01 07:39:06.210 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 452/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.8, lr: 2.962e-04, size: 384, ETA: 0:55:06
2025-08-01 07:39:09.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 452/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.4, lr: 2.956e-04, size: 288, ETA: 0:55:03
2025-08-01 07:39:13.156 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 452/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, 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: 2.950e-04, size: 544, ETA: 0:54:59
2025-08-01 07:39:14.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:39:21.424 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:39:22.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:39:22.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4783
2025-08-01 07:39:22.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4154
2025-08-01 07:39:22.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2650
2025-08-01 07:39:22.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3862
2025-08-01 07:39:22.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:39:22.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.265
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.386
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:39:22.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:39:22.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:39:22.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:39:22.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:39:23.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:39:23.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:39:24.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:39:25.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:39:25.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:39:26.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:39:27.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:39:27.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:39:28.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:39:28.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:39:28.226 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:39:28.226 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:39:28.235 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.86 ms, Average inference time: 8.39 ms

2025-08-01 07:39:28.241 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:39:28.320 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:39:28.465 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch453
2025-08-01 07:39:31.755 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 453/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.8, lr: 2.942e-04, size: 416, ETA: 0:54:54
2025-08-01 07:39:35.261 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 453/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 2.936e-04, size: 352, ETA: 0:54:51
2025-08-01 07:39:38.565 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 453/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, 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: 2.930e-04, size: 480, ETA: 0:54:47
2025-08-01 07:39:42.003 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 453/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 2.924e-04, size: 320, ETA: 0:54:44
2025-08-01 07:39:45.495 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 453/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.6, lr: 2.919e-04, size: 512, ETA: 0:54:40
2025-08-01 07:39:49.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 453/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 2.913e-04, size: 480, ETA: 0:54:37
2025-08-01 07:39:50.493 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:39:57.308 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:39:58.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:39:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4946
2025-08-01 07:39:59.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4686
2025-08-01 07:39:59.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2613
2025-08-01 07:39:59.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4082
2025-08-01 07:39:59.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:39:59.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:39:59.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-08-01 07:39:59.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-01 07:39:59.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.261
2025-08-01 07:39:59.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.408
2025-08-01 07:39:59.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:39:59.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:39:59.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:39:59.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:39:59.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:39:59.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:39:59.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:39:59.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:39:59.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:40:00.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:40:00.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:40:01.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:40:02.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:40:03.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:40:04.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:40:05.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:40:06.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:40:06.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:40:06.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 07:40:06.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 07:40:06.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:40:06.896 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.86 ms, Average inference time: 8.37 ms

2025-08-01 07:40:06.901 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:40:06.977 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:40:07.065 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch454
2025-08-01 07:40:10.238 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 454/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 1.0, cls_loss: 0.5, lr: 2.904e-04, size: 480, ETA: 0:54:32
2025-08-01 07:40:13.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 454/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 2.898e-04, size: 416, ETA: 0:54:28
2025-08-01 07:40:17.108 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 454/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.6, lr: 2.893e-04, size: 544, ETA: 0:54:25
2025-08-01 07:40:20.435 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 454/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 2.887e-04, size: 448, ETA: 0:54:21
2025-08-01 07:40:23.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 454/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 2.881e-04, size: 512, ETA: 0:54:18
2025-08-01 07:40:27.220 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 454/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 2.875e-04, size: 544, ETA: 0:54:14
2025-08-01 07:40:28.689 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:40:35.425 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:40:36.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:40:36.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4504
2025-08-01 07:40:37.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4718
2025-08-01 07:40:37.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2081
2025-08-01 07:40:37.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3768
2025-08-01 07:40:37.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:40:37.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:40:37.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-08-01 07:40:37.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-01 07:40:37.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.208
2025-08-01 07:40:37.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.377
2025-08-01 07:40:37.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:40:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:40:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:40:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:40:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:40:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:40:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:40:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:40:37.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:40:37.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:40:38.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:40:39.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:40:40.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:40:40.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:40:41.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:40:42.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:40:42.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:40:43.698 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:40:43.698 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:40:43.698 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:40:43.698 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:40:43.705 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.83 ms, Average inference time: 8.39 ms

2025-08-01 07:40:43.706 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:40:43.839 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:40:43.923 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch455
2025-08-01 07:40:47.160 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 455/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 2.867e-04, size: 352, ETA: 0:54:09
2025-08-01 07:40:50.566 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 455/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 2.861e-04, size: 416, ETA: 0:54:06
2025-08-01 07:40:53.863 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 455/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.9, lr: 2.855e-04, size: 512, ETA: 0:54:02
2025-08-01 07:40:57.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 455/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.9, lr: 2.850e-04, size: 448, ETA: 0:53:59
2025-08-01 07:41:00.447 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 455/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 2.844e-04, size: 256, ETA: 0:53:55
2025-08-01 07:41:03.715 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 455/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.1, l1_loss: 0.4, conf_loss: 2.1, cls_loss: 0.4, lr: 2.838e-04, size: 352, ETA: 0:53:52
2025-08-01 07:41:05.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:41:11.852 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:41:12.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:41:13.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3471
2025-08-01 07:41:13.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2720
2025-08-01 07:41:13.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0920
2025-08-01 07:41:13.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2370
2025-08-01 07:41:13.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:41:13.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:41:13.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-01 07:41:13.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-08-01 07:41:13.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.092
2025-08-01 07:41:13.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.237
2025-08-01 07:41:13.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:41:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:41:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:41:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:41:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:41:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:41:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:41:13.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:41:13.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:41:14.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:41:14.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:41:15.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:41:16.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:41:16.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:41:17.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:41:18.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:41:18.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:41:19.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:41:19.660 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 07:41:19.660 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 07:41:19.661 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:41:19.683 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.89 ms, Average inference time: 8.29 ms

2025-08-01 07:41:19.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:41:19.838 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:41:19.939 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch456
2025-08-01 07:41:23.004 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 456/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 2.830e-04, size: 352, ETA: 0:53:46
2025-08-01 07:41:26.342 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 456/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 2.824e-04, size: 384, ETA: 0:53:43
2025-08-01 07:41:29.819 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 456/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 2.818e-04, size: 320, ETA: 0:53:39
2025-08-01 07:41:33.180 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 456/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 2.812e-04, size: 512, ETA: 0:53:36
2025-08-01 07:41:36.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 456/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 2.807e-04, size: 288, ETA: 0:53:32
2025-08-01 07:41:39.976 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 456/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.6, lr: 2.801e-04, size: 352, ETA: 0:53:29
2025-08-01 07:41:41.530 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:41:48.250 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:41:48.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:41:49.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5195
2025-08-01 07:41:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4573
2025-08-01 07:41:49.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3181
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4316
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:41:49.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:41:49.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:41:49.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:41:49.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:41:49.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:41:49.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:41:49.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:41:49.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:41:50.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:41:50.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:41:51.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:41:51.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:41:51.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:41:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:41:52.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:41:53.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:41:53.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:41:53.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 07:41:53.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:41:53.363 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.81 ms, Average inference time: 8.26 ms

2025-08-01 07:41:53.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:41:53.481 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:41:53.593 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch457
2025-08-01 07:41:56.730 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 457/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 1.1, lr: 2.793e-04, size: 256, ETA: 0:53:24
2025-08-01 07:42:00.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 457/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 2.787e-04, size: 576, ETA: 0:53:20
2025-08-01 07:42:03.775 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 457/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 2.781e-04, size: 448, ETA: 0:53:17
2025-08-01 07:42:07.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 457/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 2.776e-04, size: 288, ETA: 0:53:13
2025-08-01 07:42:10.425 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 457/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, 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: 2.770e-04, size: 448, ETA: 0:53:10
2025-08-01 07:42:13.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 457/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 0.8, lr: 2.764e-04, size: 384, ETA: 0:53:06
2025-08-01 07:42:15.320 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:42:22.234 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:42:23.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:42:24.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5080
2025-08-01 07:42:24.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4469
2025-08-01 07:42:24.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3122
2025-08-01 07:42:24.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4224
2025-08-01 07:42:24.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:42:24.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:42:24.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-08-01 07:42:24.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 07:42:24.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-01 07:42:24.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.422
2025-08-01 07:42:24.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:42:24.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:42:24.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:42:24.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:42:24.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:42:24.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:42:24.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:42:24.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:42:24.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:42:25.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:42:25.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:42:26.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:42:27.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:42:28.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:42:29.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:42:30.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:42:30.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:42:31.703 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:42:31.704 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 07:42:31.704 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 07:42:31.704 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:42:31.713 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.88 ms, Average inference time: 8.38 ms

2025-08-01 07:42:31.714 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:42:31.854 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:42:31.939 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch458
2025-08-01 07:42:35.229 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 458/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 2.756e-04, size: 320, ETA: 0:53:01
2025-08-01 07:42:38.929 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 458/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.182s, 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: 2.750e-04, size: 320, ETA: 0:52:58
2025-08-01 07:42:42.420 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 458/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.6, conf_loss: 2.8, cls_loss: 0.7, lr: 2.744e-04, size: 544, ETA: 0:52:54
2025-08-01 07:42:45.752 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 458/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, 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: 2.739e-04, size: 352, ETA: 0:52:51
2025-08-01 07:42:49.288 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 458/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 2.733e-04, size: 544, ETA: 0:52:47
2025-08-01 07:42:52.700 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 458/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.2, cls_loss: 0.6, lr: 2.727e-04, size: 448, ETA: 0:52:44
2025-08-01 07:42:54.283 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:43:01.009 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:43:01.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:43:01.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4653
2025-08-01 07:43:01.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4588
2025-08-01 07:43:01.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2330
2025-08-01 07:43:01.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3857
2025-08-01 07:43:01.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:43:01.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.233
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.386
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:43:01.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:43:01.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:43:02.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:43:02.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:43:03.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:43:03.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:43:04.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:43:04.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:43:04.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:43:05.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:43:06.006 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:43:06.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:43:06.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:43:06.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:43:06.015 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.64 ms, Average NMS time: 0.83 ms, Average inference time: 8.46 ms

2025-08-01 07:43:06.016 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:43:06.130 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:43:06.228 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch459
2025-08-01 07:43:09.685 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 459/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 1.3, lr: 2.719e-04, size: 288, ETA: 0:52:39
2025-08-01 07:43:13.102 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 459/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 2.714e-04, size: 320, ETA: 0:52:35
2025-08-01 07:43:16.358 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 459/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 2.708e-04, size: 512, ETA: 0:52:32
2025-08-01 07:43:19.786 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 459/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 2.702e-04, size: 416, ETA: 0:52:28
2025-08-01 07:43:23.035 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 459/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 2.697e-04, size: 352, ETA: 0:52:25
2025-08-01 07:43:26.377 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 459/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 9.7, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 5.1, cls_loss: 0.7, lr: 2.691e-04, size: 576, ETA: 0:52:21
2025-08-01 07:43:27.939 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:43:34.682 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:43:35.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:43:35.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4687
2025-08-01 07:43:35.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3856
2025-08-01 07:43:35.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2179
2025-08-01 07:43:35.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3574
2025-08-01 07:43:35.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:43:35.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:43:35.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-01 07:43:35.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.218
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.357
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:43:35.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:43:35.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:43:36.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:43:36.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:43:37.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:43:37.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:43:38.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:43:38.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:43:39.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:43:39.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:43:40.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:43:40.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:43:40.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 07:43:40.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:43:40.295 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.86 ms, Average inference time: 8.47 ms

2025-08-01 07:43:40.296 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:43:40.410 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:43:40.522 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch460
2025-08-01 07:43:43.756 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 460/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.0, cls_loss: 0.6, lr: 2.683e-04, size: 352, ETA: 0:52:16
2025-08-01 07:43:46.925 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 460/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 2.677e-04, size: 480, ETA: 0:52:13
2025-08-01 07:43:50.230 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 460/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.9, lr: 2.672e-04, size: 512, ETA: 0:52:09
2025-08-01 07:43:53.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 460/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 3.7, cls_loss: 0.6, lr: 2.666e-04, size: 544, ETA: 0:52:05
2025-08-01 07:43:56.933 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 460/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.9, lr: 2.660e-04, size: 256, ETA: 0:52:02
2025-08-01 07:44:00.153 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 460/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 2.655e-04, size: 480, ETA: 0:51:58
2025-08-01 07:44:01.704 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:44:08.747 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:44:09.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:44:09.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5051
2025-08-01 07:44:09.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4260
2025-08-01 07:44:10.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2636
2025-08-01 07:44:10.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3982
2025-08-01 07:44:10.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:44:10.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:44:10.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-01 07:44:10.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-08-01 07:44:10.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:44:10.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:44:10.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:44:11.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:44:11.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:44:12.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:44:12.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:44:13.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:44:13.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:44:14.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:44:15.079 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:44:15.079 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 07:44:15.079 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 07:44:15.079 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:44:15.086 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.83 ms, Average inference time: 8.32 ms

2025-08-01 07:44:15.087 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:44:15.171 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:44:15.249 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch461
2025-08-01 07:44:18.579 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 461/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 9.5, iou_loss: 3.3, l1_loss: 1.5, conf_loss: 3.9, cls_loss: 0.8, lr: 2.647e-04, size: 512, ETA: 0:51:53
2025-08-01 07:44:22.077 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 461/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.8, lr: 2.641e-04, size: 416, ETA: 0:51:50
2025-08-01 07:44:25.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 461/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.6, lr: 2.635e-04, size: 352, ETA: 0:51:47
2025-08-01 07:44:28.948 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 461/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 2.630e-04, size: 384, ETA: 0:51:43
2025-08-01 07:44:32.603 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 461/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.6, lr: 2.624e-04, size: 576, ETA: 0:51:40
2025-08-01 07:44:35.988 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 461/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.5, cls_loss: 0.5, lr: 2.619e-04, size: 352, ETA: 0:51:36
2025-08-01 07:44:37.385 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:44:44.368 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:44:45.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:44:46.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4662
2025-08-01 07:44:46.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3831
2025-08-01 07:44:46.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2304
2025-08-01 07:44:46.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3599
2025-08-01 07:44:46.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:44:46.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:44:46.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-01 07:44:46.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-01 07:44:46.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.230
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.360
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:44:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:44:47.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:44:48.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:44:49.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:44:50.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:44:51.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:44:51.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:44:52.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:44:53.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:44:54.377 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:44:54.378 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:44:54.378 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 07:44:54.378 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:44:54.388 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.89 ms, Average inference time: 8.46 ms

2025-08-01 07:44:54.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:44:54.510 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:44:54.591 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch462
2025-08-01 07:44:57.715 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 462/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 2.611e-04, size: 288, ETA: 0:51:31
2025-08-01 07:45:01.183 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 462/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, 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: 2.605e-04, size: 320, ETA: 0:51:27
2025-08-01 07:45:04.686 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 462/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 2.600e-04, size: 256, ETA: 0:51:24
2025-08-01 07:45:08.135 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 462/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, 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: 2.594e-04, size: 512, ETA: 0:51:21
2025-08-01 07:45:11.422 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 462/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, 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: 2.588e-04, size: 384, ETA: 0:51:17
2025-08-01 07:45:14.898 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 462/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.171s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 2.583e-04, size: 576, ETA: 0:51:14
2025-08-01 07:45:16.435 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:45:23.492 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:45:24.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:45:24.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4305
2025-08-01 07:45:24.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4066
2025-08-01 07:45:24.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1952
2025-08-01 07:45:24.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3441
2025-08-01 07:45:24.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:45:24.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:45:24.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 07:45:24.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 07:45:24.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-08-01 07:45:24.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 07:45:24.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:45:24.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:45:24.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:45:24.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:45:24.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:45:24.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:45:24.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:45:24.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:45:24.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:45:25.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:45:25.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:45:26.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:45:26.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:45:27.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:45:27.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:45:28.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:45:28.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:45:28.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:45:28.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:45:28.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 07:45:28.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:45:28.951 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.83 ms, Average inference time: 8.14 ms

2025-08-01 07:45:28.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:45:29.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:45:29.204 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch463
2025-08-01 07:45:32.513 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 463/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 0.7, lr: 2.575e-04, size: 544, ETA: 0:51:08
2025-08-01 07:45:35.918 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 463/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 2.569e-04, size: 352, ETA: 0:51:05
2025-08-01 07:45:39.115 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 463/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 2.564e-04, size: 416, ETA: 0:51:01
2025-08-01 07:45:42.455 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 463/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.7, lr: 2.558e-04, size: 416, ETA: 0:50:58
2025-08-01 07:45:45.671 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 463/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.6, lr: 2.553e-04, size: 352, ETA: 0:50:54
2025-08-01 07:45:48.944 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 463/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, 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: 2.547e-04, size: 448, ETA: 0:50:51
2025-08-01 07:45:50.350 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:45:57.112 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:45:58.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:45:58.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4908
2025-08-01 07:45:58.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4282
2025-08-01 07:45:58.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2775
2025-08-01 07:45:58.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3988
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.277
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.399
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:45:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:45:58.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:45:58.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:45:58.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:45:58.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:45:58.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:45:59.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:46:00.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:46:01.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:46:02.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:46:03.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:46:03.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:46:04.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:46:05.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:46:06.448 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:46:06.448 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:46:06.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 07:46:06.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:46:06.456 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.90 ms, Average inference time: 8.31 ms

2025-08-01 07:46:06.458 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:46:06.583 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:46:06.665 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch464
2025-08-01 07:46:09.798 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 464/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.9, lr: 2.539e-04, size: 352, ETA: 0:50:46
2025-08-01 07:46:12.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 464/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, 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: 2.534e-04, size: 256, ETA: 0:50:42
2025-08-01 07:46:16.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 464/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 2.528e-04, size: 576, ETA: 0:50:39
2025-08-01 07:46:19.733 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 464/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 2.523e-04, size: 416, ETA: 0:50:35
2025-08-01 07:46:23.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 464/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 2.517e-04, size: 288, ETA: 0:50:32
2025-08-01 07:46:26.264 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 464/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.5, lr: 2.512e-04, size: 352, ETA: 0:50:28
2025-08-01 07:46:27.746 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:46:34.550 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:46:35.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:46:35.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4715
2025-08-01 07:46:36.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3966
2025-08-01 07:46:36.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2429
2025-08-01 07:46:36.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3703
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.243
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:46:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:46:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:46:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:46:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:46:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:46:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:46:36.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:46:37.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:46:38.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:46:38.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:46:39.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:46:40.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:46:40.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:46:41.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:46:42.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:46:42.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:46:42.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 07:46:42.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:46:42.318 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.87 ms, Average inference time: 8.43 ms

2025-08-01 07:46:42.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:46:42.397 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:46:42.477 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch465
2025-08-01 07:46:45.680 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 465/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 2.504e-04, size: 384, ETA: 0:50:23
2025-08-01 07:46:49.120 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 465/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.6, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 2.499e-04, size: 480, ETA: 0:50:20
2025-08-01 07:46:52.471 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 465/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 2.493e-04, size: 352, ETA: 0:50:16
2025-08-01 07:46:55.901 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 465/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 2.488e-04, size: 416, ETA: 0:50:13
2025-08-01 07:46:59.236 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 465/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 2.482e-04, size: 544, ETA: 0:50:09
2025-08-01 07:47:02.539 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 465/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 2.477e-04, size: 448, ETA: 0:50:06
2025-08-01 07:47:04.042 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:47:10.815 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:47:11.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:47:12.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4222
2025-08-01 07:47:12.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4322
2025-08-01 07:47:12.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1966
2025-08-01 07:47:12.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3503
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.197
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.350
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:47:12.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:47:12.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:47:12.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:47:12.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:47:12.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:47:12.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:47:12.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:47:12.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:47:13.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:47:14.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:47:14.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:47:15.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:47:15.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:47:16.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:47:16.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:47:17.378 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:47:17.379 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:47:17.379 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 07:47:17.379 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:47:17.386 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.84 ms, Average inference time: 8.23 ms

2025-08-01 07:47:17.387 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:47:17.464 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:47:17.543 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch466
2025-08-01 07:47:20.912 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 466/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 2.469e-04, size: 288, ETA: 0:50:00
2025-08-01 07:47:24.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 466/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 2.463e-04, size: 480, ETA: 0:49:57
2025-08-01 07:47:27.453 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 466/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 2.458e-04, size: 352, ETA: 0:49:53
2025-08-01 07:47:30.845 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 466/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.4, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 2.453e-04, size: 320, ETA: 0:49:50
2025-08-01 07:47:34.122 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 466/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.9, lr: 2.447e-04, size: 416, ETA: 0:49:46
2025-08-01 07:47:37.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 466/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.8, lr: 2.442e-04, size: 320, ETA: 0:49:43
2025-08-01 07:47:38.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:47:45.800 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:47:47.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:47:47.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4963
2025-08-01 07:47:48.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4180
2025-08-01 07:47:48.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2537
2025-08-01 07:47:48.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3893
2025-08-01 07:47:48.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:47:48.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:47:48.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-01 07:47:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-01 07:47:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-01 07:47:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.389
2025-08-01 07:47:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:47:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:47:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:47:48.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:47:48.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:47:48.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:47:48.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:47:48.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:47:48.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:47:49.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:47:49.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:47:50.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:47:51.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:47:52.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:47:53.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:47:54.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:47:55.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:47:55.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:47:55.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:47:55.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 07:47:55.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:47:55.960 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.86 ms, Average inference time: 8.35 ms

2025-08-01 07:47:55.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:47:56.040 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:47:56.123 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch467
2025-08-01 07:47:59.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 467/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 2.434e-04, size: 384, ETA: 0:49:38
2025-08-01 07:48:02.833 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 467/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 2.429e-04, size: 544, ETA: 0:49:34
2025-08-01 07:48:06.216 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 467/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.6, lr: 2.423e-04, size: 448, ETA: 0:49:31
2025-08-01 07:48:09.526 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 467/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, 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: 2.418e-04, size: 480, ETA: 0:49:27
2025-08-01 07:48:13.040 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 467/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 0.9, lr: 2.412e-04, size: 576, ETA: 0:49:24
2025-08-01 07:48:16.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 467/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 2.407e-04, size: 448, ETA: 0:49:20
2025-08-01 07:48:18.021 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:48:24.977 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:48:25.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:48:26.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4973
2025-08-01 07:48:26.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4556
2025-08-01 07:48:26.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2868
2025-08-01 07:48:26.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4132
2025-08-01 07:48:26.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.413
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:48:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:48:26.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:48:26.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:48:27.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:48:27.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:48:28.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:48:29.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:48:29.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:48:30.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:48:31.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:48:31.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:48:32.380 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:48:32.380 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:48:32.380 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 07:48:32.381 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:48:32.388 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.66 ms, Average NMS time: 0.85 ms, Average inference time: 8.51 ms

2025-08-01 07:48:32.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:48:32.483 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:48:32.575 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch468
2025-08-01 07:48:35.824 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 468/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 2.399e-04, size: 384, ETA: 0:49:15
2025-08-01 07:48:39.120 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 468/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.9, lr: 2.394e-04, size: 256, ETA: 0:49:12
2025-08-01 07:48:42.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 468/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 1.2, lr: 2.389e-04, size: 512, ETA: 0:49:08
2025-08-01 07:48:45.925 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 468/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.7, lr: 2.383e-04, size: 544, ETA: 0:49:05
2025-08-01 07:48:49.360 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 468/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.168s, 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: 2.378e-04, size: 576, ETA: 0:49:01
2025-08-01 07:48:52.803 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 468/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 3.8, cls_loss: 0.9, lr: 2.372e-04, size: 256, ETA: 0:48:58
2025-08-01 07:48:54.390 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:49:01.166 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:49:02.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:49:02.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3178
2025-08-01 07:49:02.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2835
2025-08-01 07:49:02.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1270
2025-08-01 07:49:02.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2428
2025-08-01 07:49:02.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:49:02.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:49:02.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-01 07:49:02.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.283
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.127
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.243
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:49:02.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:49:03.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:49:03.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:49:04.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:49:05.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:49:05.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:49:06.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:49:07.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:49:07.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:49:08.243 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:49:08.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 07:49:08.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 07:49:08.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:49:08.252 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.91 ms, Average inference time: 8.48 ms

2025-08-01 07:49:08.253 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:49:08.375 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:49:08.504 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch469
2025-08-01 07:49:11.933 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 469/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 1.7, cls_loss: 0.7, lr: 2.365e-04, size: 512, ETA: 0:48:53
2025-08-01 07:49:15.428 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 469/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 2.359e-04, size: 480, ETA: 0:48:49
2025-08-01 07:49:19.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 469/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.6, lr: 2.354e-04, size: 352, ETA: 0:48:46
2025-08-01 07:49:22.273 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 469/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 2.349e-04, size: 288, ETA: 0:48:42
2025-08-01 07:49:25.562 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 469/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 1.1, lr: 2.343e-04, size: 416, ETA: 0:48:39
2025-08-01 07:49:28.936 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 469/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 10.1, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 4.7, cls_loss: 1.1, lr: 2.338e-04, size: 544, ETA: 0:48:35
2025-08-01 07:49:30.531 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:49:37.186 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:49:38.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:49:39.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4522
2025-08-01 07:49:39.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3332
2025-08-01 07:49:39.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1837
2025-08-01 07:49:39.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3230
2025-08-01 07:49:39.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:49:39.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.323
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:49:39.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:49:39.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:49:39.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:49:40.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:49:41.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:49:42.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:49:43.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:49:43.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:49:44.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:49:45.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:49:46.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:49:47.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:49:47.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 07:49:47.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 07:49:47.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:49:47.888 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.93 ms, Average inference time: 8.30 ms

2025-08-01 07:49:47.889 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:49:48.046 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:49:48.130 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch470
2025-08-01 07:49:51.387 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 470/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 2.6, cls_loss: 0.6, lr: 2.330e-04, size: 352, ETA: 0:48:30
2025-08-01 07:49:54.765 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 470/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, 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: 2.325e-04, size: 416, ETA: 0:48:27
2025-08-01 07:49:57.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 470/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 2.320e-04, size: 384, ETA: 0:48:23
2025-08-01 07:50:01.255 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 470/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 2.315e-04, size: 384, ETA: 0:48:20
2025-08-01 07:50:04.493 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 470/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.9, lr: 2.309e-04, size: 256, ETA: 0:48:16
2025-08-01 07:50:07.898 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 470/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 2.304e-04, size: 448, ETA: 0:48:13
2025-08-01 07:50:09.430 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:50:16.143 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:50:17.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:50:17.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5468
2025-08-01 07:50:17.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4946
2025-08-01 07:50:17.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3252
2025-08-01 07:50:17.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4555
2025-08-01 07:50:17.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:50:17.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:50:17.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-01 07:50:17.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-08-01 07:50:17.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.325
2025-08-01 07:50:17.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-08-01 07:50:17.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:50:17.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:50:17.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:50:17.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:50:17.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:50:17.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:50:17.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:50:17.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:50:17.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:50:18.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:50:19.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:50:19.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:50:20.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:50:21.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:50:21.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:50:22.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:50:23.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:50:23.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:50:23.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-01 07:50:23.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-01 07:50:23.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:50:23.874 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.88 ms, Average inference time: 8.36 ms

2025-08-01 07:50:23.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:50:23.996 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:50:24.112 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch471
2025-08-01 07:50:27.528 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 471/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 2.296e-04, size: 416, ETA: 0:48:08
2025-08-01 07:50:30.944 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 471/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.8, lr: 2.291e-04, size: 544, ETA: 0:48:04
2025-08-01 07:50:34.339 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 471/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.8, lr: 2.286e-04, size: 384, ETA: 0:48:01
2025-08-01 07:50:37.588 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 471/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 2.281e-04, size: 448, ETA: 0:47:57
2025-08-01 07:50:41.142 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 471/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 2.275e-04, size: 576, ETA: 0:47:54
2025-08-01 07:50:44.765 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 471/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 2.270e-04, size: 576, ETA: 0:47:50
2025-08-01 07:50:46.342 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:50:53.076 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:50:53.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:50:54.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4895
2025-08-01 07:50:54.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3107
2025-08-01 07:50:54.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2745
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3582
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.274
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.358
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:50:54.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:50:54.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:50:54.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:50:54.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:50:54.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:50:54.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:50:54.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:50:54.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:50:55.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:50:55.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:50:56.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:50:56.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:50:57.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:50:58.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:50:58.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:50:59.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:51:00.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:51:00.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:51:00.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 07:51:00.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:51:00.013 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.87 ms, Average inference time: 8.37 ms

2025-08-01 07:51:00.014 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:51:00.095 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:51:00.173 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch472
2025-08-01 07:51:03.451 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 472/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 1.0, lr: 2.263e-04, size: 384, ETA: 0:47:45
2025-08-01 07:51:06.810 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 472/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 2.257e-04, size: 480, ETA: 0:47:42
2025-08-01 07:51:10.186 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 472/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 2.252e-04, size: 384, ETA: 0:47:38
2025-08-01 07:51:13.646 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 472/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.9, lr: 2.247e-04, size: 256, ETA: 0:47:35
2025-08-01 07:51:17.176 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 472/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 2.242e-04, size: 256, ETA: 0:47:32
2025-08-01 07:51:20.578 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 472/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.006s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 2.236e-04, size: 576, ETA: 0:47:28
2025-08-01 07:51:22.070 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:51:28.809 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:51:29.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:51:30.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4684
2025-08-01 07:51:30.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3621
2025-08-01 07:51:30.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2139
2025-08-01 07:51:30.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3482
2025-08-01 07:51:30.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:51:30.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:51:30.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-01 07:51:30.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-08-01 07:51:30.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.214
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.348
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:51:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:51:31.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:51:31.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:51:32.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:51:33.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:51:33.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:51:34.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:51:35.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:51:35.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:51:36.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:51:36.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:51:36.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 07:51:36.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:51:36.312 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.85 ms, Average inference time: 8.28 ms

2025-08-01 07:51:36.313 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:51:36.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:51:36.557 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch473
2025-08-01 07:51:39.727 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 473/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 2.229e-04, size: 448, ETA: 0:47:23
2025-08-01 07:51:43.014 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 473/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 2.224e-04, size: 384, ETA: 0:47:19
2025-08-01 07:51:46.363 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 473/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 1.7, cls_loss: 0.8, lr: 2.219e-04, size: 576, ETA: 0:47:16
2025-08-01 07:51:49.770 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 473/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.9, lr: 2.213e-04, size: 384, ETA: 0:47:12
2025-08-01 07:51:53.158 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 473/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, 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: 2.208e-04, size: 544, ETA: 0:47:09
2025-08-01 07:51:56.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 473/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 2.203e-04, size: 544, ETA: 0:47:05
2025-08-01 07:51:58.050 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:52:04.706 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:52:05.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:52:05.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5053
2025-08-01 07:52:06.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4193
2025-08-01 07:52:06.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2253
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3833
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.225
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.383
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:52:06.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:52:06.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:52:06.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:52:06.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:52:06.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:52:06.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:52:06.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:52:06.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:52:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:52:07.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:52:08.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:52:09.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:52:09.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:52:10.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:52:11.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:52:11.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:52:11.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:52:11.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:52:11.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:52:11.786 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.84 ms, Average inference time: 8.40 ms

2025-08-01 07:52:11.788 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:52:11.906 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:52:11.987 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch474
2025-08-01 07:52:15.191 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 474/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 2.196e-04, size: 448, ETA: 0:47:00
2025-08-01 07:52:18.488 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 474/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 2.190e-04, size: 448, ETA: 0:46:57
2025-08-01 07:52:21.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 474/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, 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: 2.185e-04, size: 320, ETA: 0:46:53
2025-08-01 07:52:25.194 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 474/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 2.180e-04, size: 384, ETA: 0:46:50
2025-08-01 07:52:28.587 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 474/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 0.6, lr: 2.175e-04, size: 544, ETA: 0:46:46
2025-08-01 07:52:31.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 474/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 2.170e-04, size: 288, ETA: 0:46:43
2025-08-01 07:52:33.333 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:52:40.073 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:52:40.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:52:41.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5224
2025-08-01 07:52:41.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4046
2025-08-01 07:52:41.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3202
2025-08-01 07:52:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4157
2025-08-01 07:52:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:52:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:52:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-01 07:52:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-08-01 07:52:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-08-01 07:52:41.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.416
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:52:41.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:52:41.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:52:42.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:52:42.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:52:43.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:52:43.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:52:44.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:52:45.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:52:45.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:52:46.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:52:46.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 07:52:46.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 07:52:46.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:52:46.091 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.84 ms, Average inference time: 8.30 ms

2025-08-01 07:52:46.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:52:46.168 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:52:46.248 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch475
2025-08-01 07:52:49.878 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 475/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 2.162e-04, size: 576, ETA: 0:46:38
2025-08-01 07:52:53.309 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 475/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 2.157e-04, size: 256, ETA: 0:46:34
2025-08-01 07:52:56.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 475/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, 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: 2.152e-04, size: 416, ETA: 0:46:31
2025-08-01 07:53:00.115 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 475/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 2.147e-04, size: 416, ETA: 0:46:27
2025-08-01 07:53:03.425 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 475/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.005s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 2.142e-04, size: 480, ETA: 0:46:24
2025-08-01 07:53:06.793 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 475/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.7, lr: 2.137e-04, size: 480, ETA: 0:46:20
2025-08-01 07:53:08.344 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:53:14.968 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:53:16.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:53:16.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3279
2025-08-01 07:53:16.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2201
2025-08-01 07:53:17.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1410
2025-08-01 07:53:17.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2297
2025-08-01 07:53:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:53:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:53:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-08-01 07:53:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.220
2025-08-01 07:53:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.141
2025-08-01 07:53:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.230
2025-08-01 07:53:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:53:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:53:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:53:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:53:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:53:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:53:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:53:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:53:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:53:18.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:53:18.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:53:19.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:53:20.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:53:21.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:53:22.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:53:23.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:53:24.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:53:25.264 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:53:25.264 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 07:53:25.264 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-08-01 07:53:25.264 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:53:25.271 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.87 ms, Average inference time: 8.48 ms

2025-08-01 07:53:25.273 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:53:25.355 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:53:25.435 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch476
2025-08-01 07:53:28.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 476/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 2.129e-04, size: 320, ETA: 0:46:15
2025-08-01 07:53:31.803 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 476/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 2.124e-04, size: 576, ETA: 0:46:12
2025-08-01 07:53:35.223 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 476/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.9, lr: 2.119e-04, size: 416, ETA: 0:46:08
2025-08-01 07:53:38.615 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 476/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.2, cls_loss: 0.8, lr: 2.114e-04, size: 512, ETA: 0:46:05
2025-08-01 07:53:42.152 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 476/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, 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: 2.109e-04, size: 448, ETA: 0:46:01
2025-08-01 07:53:45.577 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 476/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 2.104e-04, size: 576, ETA: 0:45:58
2025-08-01 07:53:47.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:53:54.037 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:53:55.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:53:56.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3992
2025-08-01 07:53:56.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2937
2025-08-01 07:53:56.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1911
2025-08-01 07:53:56.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2947
2025-08-01 07:53:56.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:53:56.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.295
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:53:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:53:56.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:53:57.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:53:58.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:53:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:53:59.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:54:00.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:54:01.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:54:02.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:54:02.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:54:03.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:54:03.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 07:54:03.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 07:54:03.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:54:03.699 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.62 ms, Average NMS time: 0.89 ms, Average inference time: 8.50 ms

2025-08-01 07:54:03.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:54:03.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:54:03.868 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch477
2025-08-01 07:54:07.031 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 477/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.9, lr: 2.097e-04, size: 480, ETA: 0:45:53
2025-08-01 07:54:10.508 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 477/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 2.092e-04, size: 448, ETA: 0:45:49
2025-08-01 07:54:13.965 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 477/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 2.087e-04, size: 320, ETA: 0:45:46
2025-08-01 07:54:17.183 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 477/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 2.082e-04, size: 256, ETA: 0:45:42
2025-08-01 07:54:20.376 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 477/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 2.076e-04, size: 320, ETA: 0:45:39
2025-08-01 07:54:23.611 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 477/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 2.071e-04, size: 448, ETA: 0:45:35
2025-08-01 07:54:25.044 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:54:31.755 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:54:32.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:54:33.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4963
2025-08-01 07:54:33.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4518
2025-08-01 07:54:33.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3012
2025-08-01 07:54:33.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4164
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.416
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:54:33.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:54:33.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:54:33.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:54:33.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:54:33.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:54:33.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:54:33.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:54:33.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:54:34.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:54:35.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:54:35.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:54:36.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:54:37.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:54:37.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:54:38.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:54:39.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:54:39.187 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 07:54:39.187 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 07:54:39.187 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:54:39.194 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.84 ms, Average inference time: 8.35 ms

2025-08-01 07:54:39.195 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:54:39.278 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:54:39.359 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch478
2025-08-01 07:54:42.717 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 478/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 2.064e-04, size: 352, ETA: 0:45:30
2025-08-01 07:54:45.913 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 478/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, 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: 2.059e-04, size: 256, ETA: 0:45:27
2025-08-01 07:54:49.375 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 478/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 2.054e-04, size: 320, ETA: 0:45:23
2025-08-01 07:54:52.783 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 478/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 2.049e-04, size: 576, ETA: 0:45:20
2025-08-01 07:54:56.249 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 478/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 2.044e-04, size: 320, ETA: 0:45:16
2025-08-01 07:54:59.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 478/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 2.039e-04, size: 480, ETA: 0:45:13
2025-08-01 07:55:01.132 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:55:07.873 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:55:08.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:55:08.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3147
2025-08-01 07:55:08.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3212
2025-08-01 07:55:08.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1009
2025-08-01 07:55:08.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2456
2025-08-01 07:55:08.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:55:08.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.101
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.246
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:55:08.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:55:08.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:55:08.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:55:08.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:55:09.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:55:09.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:55:10.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:55:10.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:55:11.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:55:11.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:55:12.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:55:12.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:55:12.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:55:12.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 07:55:12.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-08-01 07:55:12.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:55:12.956 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.82 ms, Average inference time: 8.22 ms

2025-08-01 07:55:12.957 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:55:13.076 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:55:13.185 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch479
2025-08-01 07:55:16.538 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 479/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 2.032e-04, size: 576, ETA: 0:45:08
2025-08-01 07:55:20.078 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 479/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 2.027e-04, size: 544, ETA: 0:45:04
2025-08-01 07:55:23.744 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 479/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 2.022e-04, size: 256, ETA: 0:45:01
2025-08-01 07:55:27.227 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 479/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, 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: 2.017e-04, size: 288, ETA: 0:44:58
2025-08-01 07:55:30.575 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 479/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, 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: 2.012e-04, size: 480, ETA: 0:44:54
2025-08-01 07:55:34.114 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 479/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 2.007e-04, size: 480, ETA: 0:44:51
2025-08-01 07:55:35.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:55:42.431 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:55:43.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:55:43.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4638
2025-08-01 07:55:43.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4258
2025-08-01 07:55:43.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2444
2025-08-01 07:55:43.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3780
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.378
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:55:43.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:55:43.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:55:43.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:55:43.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:55:43.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:55:43.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:55:44.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:55:44.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:55:45.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:55:45.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:55:46.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:55:46.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:55:47.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:55:47.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:55:48.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:55:48.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:55:48.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 07:55:48.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:55:48.180 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.82 ms, Average inference time: 8.31 ms

2025-08-01 07:55:48.181 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:55:48.297 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:55:48.431 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch480
2025-08-01 07:55:51.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 480/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 2.000e-04, size: 320, ETA: 0:44:46
2025-08-01 07:55:54.799 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 480/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.995e-04, size: 480, ETA: 0:44:42
2025-08-01 07:55:58.078 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 480/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 1.5, lr: 1.990e-04, size: 256, ETA: 0:44:39
2025-08-01 07:56:01.295 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 480/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, 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: 1.985e-04, size: 352, ETA: 0:44:35
2025-08-01 07:56:04.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 480/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.980e-04, size: 352, ETA: 0:44:32
2025-08-01 07:56:07.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 480/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.975e-04, size: 320, ETA: 0:44:28
2025-08-01 07:56:09.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:56:16.032 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:56:16.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:56:17.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4488
2025-08-01 07:56:17.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3645
2025-08-01 07:56:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2440
2025-08-01 07:56:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3524
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:56:17.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:56:17.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:56:17.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:56:17.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:56:17.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:56:17.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:56:18.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:56:19.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:56:19.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:56:20.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:56:21.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:56:21.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:56:22.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:56:23.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:56:23.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:56:23.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:56:23.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 07:56:23.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:56:23.759 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.88 ms, Average inference time: 8.29 ms

2025-08-01 07:56:23.760 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:56:23.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:56:23.983 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch481
2025-08-01 07:56:27.205 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 481/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.9, lr: 1.968e-04, size: 512, ETA: 0:44:23
2025-08-01 07:56:30.419 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 481/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, 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: 1.963e-04, size: 320, ETA: 0:44:19
2025-08-01 07:56:33.668 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 481/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, 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: 1.958e-04, size: 256, ETA: 0:44:16
2025-08-01 07:56:36.950 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 481/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, 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: 1.953e-04, size: 448, ETA: 0:44:12
2025-08-01 07:56:40.273 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 481/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.948e-04, size: 288, ETA: 0:44:09
2025-08-01 07:56:43.657 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 481/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 1.943e-04, size: 576, ETA: 0:44:05
2025-08-01 07:56:45.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:56:52.056 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:56:52.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:56:53.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4637
2025-08-01 07:56:53.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3982
2025-08-01 07:56:53.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2125
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3581
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.358
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:56:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:56:53.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:56:53.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:56:53.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:56:53.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:56:54.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:56:54.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:56:55.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:56:55.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:56:56.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:56:57.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:56:57.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:56:58.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:56:59.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:56:59.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:56:59.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 07:56:59.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:56:59.119 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.82 ms, Average inference time: 8.39 ms

2025-08-01 07:56:59.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:56:59.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:56:59.329 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch482
2025-08-01 07:57:02.681 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 482/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.936e-04, size: 544, ETA: 0:44:00
2025-08-01 07:57:05.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 482/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.931e-04, size: 448, ETA: 0:43:57
2025-08-01 07:57:09.320 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 482/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 1.0, lr: 1.927e-04, size: 320, ETA: 0:43:53
2025-08-01 07:57:12.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 482/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 1.922e-04, size: 352, ETA: 0:43:50
2025-08-01 07:57:15.883 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 482/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.917e-04, size: 352, ETA: 0:43:46
2025-08-01 07:57:19.048 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 482/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 11.2, iou_loss: 4.1, l1_loss: 1.5, conf_loss: 4.6, cls_loss: 1.1, lr: 1.912e-04, size: 384, ETA: 0:43:43
2025-08-01 07:57:20.551 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:57:27.223 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:57:27.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:57:28.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4695
2025-08-01 07:57:28.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3761
2025-08-01 07:57:28.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2341
2025-08-01 07:57:28.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3599
2025-08-01 07:57:28.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:57:28.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:57:28.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-08-01 07:57:28.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-01 07:57:28.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.234
2025-08-01 07:57:28.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.360
2025-08-01 07:57:28.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:57:28.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:57:28.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:57:28.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:57:28.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:57:28.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:57:28.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:57:28.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:57:28.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:57:28.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:57:29.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:57:29.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:57:30.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:57:30.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:57:31.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:57:31.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:57:32.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:57:32.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:57:32.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 07:57:32.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 07:57:32.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:57:32.670 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.81 ms, Average inference time: 8.27 ms

2025-08-01 07:57:32.671 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:57:32.792 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:57:32.935 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch483
2025-08-01 07:57:36.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 483/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.905e-04, size: 384, ETA: 0:43:38
2025-08-01 07:57:39.576 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 483/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, 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: 1.900e-04, size: 576, ETA: 0:43:34
2025-08-01 07:57:43.142 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 483/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.895e-04, size: 512, ETA: 0:43:31
2025-08-01 07:57:46.456 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 483/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 16.3, iou_loss: 3.9, l1_loss: 1.3, conf_loss: 9.7, cls_loss: 1.3, lr: 1.890e-04, size: 256, ETA: 0:43:27
2025-08-01 07:57:49.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 483/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.3, l1_loss: 0.4, conf_loss: 1.2, cls_loss: 0.6, lr: 1.886e-04, size: 320, ETA: 0:43:24
2025-08-01 07:57:53.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 483/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 1.1, lr: 1.881e-04, size: 288, ETA: 0:43:20
2025-08-01 07:57:54.543 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:58:01.367 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:58:02.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:58:03.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3553
2025-08-01 07:58:03.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2874
2025-08-01 07:58:03.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1537
2025-08-01 07:58:03.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2654
2025-08-01 07:58:03.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:58:03.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:58:03.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-08-01 07:58:03.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.154
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.265
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:58:03.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:58:05.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:58:06.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:58:07.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:58:08.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:58:09.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:58:10.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:58:12.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:58:13.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:58:14.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:58:14.383 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 07:58:14.383 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 07:58:14.383 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:58:14.390 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.86 ms, Average inference time: 8.37 ms

2025-08-01 07:58:14.392 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:58:14.471 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:58:14.568 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch484
2025-08-01 07:58:17.924 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 484/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 0.7, lr: 1.874e-04, size: 256, ETA: 0:43:15
2025-08-01 07:58:21.294 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 484/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.869e-04, size: 480, ETA: 0:43:12
2025-08-01 07:58:24.590 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 484/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 1.864e-04, size: 256, ETA: 0:43:08
2025-08-01 07:58:27.835 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 484/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.859e-04, size: 320, ETA: 0:43:05
2025-08-01 07:58:30.959 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 484/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.9, lr: 1.855e-04, size: 384, ETA: 0:43:01
2025-08-01 07:58:34.333 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 484/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 1.850e-04, size: 576, ETA: 0:42:58
2025-08-01 07:58:35.957 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:58:42.719 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:58:43.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:58:44.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4651
2025-08-01 07:58:44.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4119
2025-08-01 07:58:44.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2000
2025-08-01 07:58:44.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3590
2025-08-01 07:58:44.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:58:44.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:58:44.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-01 07:58:44.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-01 07:58:44.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.200
2025-08-01 07:58:44.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-08-01 07:58:44.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:58:44.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:58:44.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:58:44.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:58:44.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:58:44.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:58:44.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:58:44.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:58:44.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:58:45.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:58:45.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:58:46.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:58:47.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:58:47.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:58:48.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:58:49.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:58:50.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:58:50.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:58:50.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 07:58:50.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 07:58:50.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:58:50.705 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.88 ms, Average inference time: 8.29 ms

2025-08-01 07:58:50.707 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:58:50.784 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:58:50.863 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch485
2025-08-01 07:58:54.158 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 485/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.4, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 1.5, lr: 1.843e-04, size: 320, ETA: 0:42:53
2025-08-01 07:58:58.047 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 485/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.192s, 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: 1.838e-04, size: 512, ETA: 0:42:49
2025-08-01 07:59:01.462 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 485/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 1.7, cls_loss: 0.6, lr: 1.833e-04, size: 512, ETA: 0:42:46
2025-08-01 07:59:04.678 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 485/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.829e-04, size: 288, ETA: 0:42:42
2025-08-01 07:59:07.886 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 485/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.005s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 1.824e-04, size: 288, ETA: 0:42:39
2025-08-01 07:59:11.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 485/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 1.0, lr: 1.819e-04, size: 320, ETA: 0:42:35
2025-08-01 07:59:12.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:59:19.342 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:59:20.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:59:20.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4443
2025-08-01 07:59:20.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3439
2025-08-01 07:59:20.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2191
2025-08-01 07:59:20.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3358
2025-08-01 07:59:20.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:59:20.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.219
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:59:20.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:59:20.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:59:21.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:59:21.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:59:22.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:59:23.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:59:23.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 07:59:24.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 07:59:24.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 07:59:25.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 07:59:25.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 07:59:25.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 07:59:25.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 07:59:25.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 07:59:25.944 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.89 ms, Average inference time: 8.41 ms

2025-08-01 07:59:25.945 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:59:26.029 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:59:26.135 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch486
2025-08-01 07:59:29.280 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 486/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.812e-04, size: 480, ETA: 0:42:30
2025-08-01 07:59:32.635 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 486/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 1.807e-04, size: 448, ETA: 0:42:27
2025-08-01 07:59:35.954 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 486/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.803e-04, size: 448, ETA: 0:42:23
2025-08-01 07:59:39.379 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 486/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.6, lr: 1.798e-04, size: 384, ETA: 0:42:20
2025-08-01 07:59:42.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 486/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.003s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.793e-04, size: 288, ETA: 0:42:16
2025-08-01 07:59:46.182 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 486/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 1.789e-04, size: 352, ETA: 0:42:13
2025-08-01 07:59:47.701 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 07:59:54.478 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 07:59:55.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 07:59:55.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5053
2025-08-01 07:59:55.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4605
2025-08-01 07:59:56.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3285
2025-08-01 07:59:56.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4314
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.329
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 07:59:56.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 07:59:56.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 07:59:56.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 07:59:56.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 07:59:56.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 07:59:56.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 07:59:57.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 07:59:58.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 07:59:58.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 07:59:59.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:00:00.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:00:00.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:00:01.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:00:02.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:00:02.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:00:02.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 08:00:02.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:00:02.098 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.85 ms, Average inference time: 8.31 ms

2025-08-01 08:00:02.099 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:00:02.192 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:00:02.270 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch487
2025-08-01 08:00:05.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 487/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 1.782e-04, size: 288, ETA: 0:42:08
2025-08-01 08:00:08.953 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 487/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.777e-04, size: 320, ETA: 0:42:04
2025-08-01 08:00:12.685 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 487/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.772e-04, size: 320, ETA: 0:42:01
2025-08-01 08:00:16.097 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 487/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, 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: 1.768e-04, size: 512, ETA: 0:41:58
2025-08-01 08:00:19.760 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 487/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.763e-04, size: 480, ETA: 0:41:54
2025-08-01 08:00:23.142 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 487/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 1.758e-04, size: 288, ETA: 0:41:51
2025-08-01 08:00:24.580 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:00:31.287 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:00:31.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:00:32.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4507
2025-08-01 08:00:32.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4091
2025-08-01 08:00:32.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2390
2025-08-01 08:00:32.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3663
2025-08-01 08:00:32.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:00:32.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:00:32.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 08:00:32.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-08-01 08:00:32.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.239
2025-08-01 08:00:32.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.366
2025-08-01 08:00:32.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:00:32.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:00:32.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:00:32.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:00:32.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:00:32.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:00:32.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:00:32.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:00:32.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:00:33.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:00:33.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:00:34.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:00:34.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:00:35.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:00:35.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:00:36.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:00:36.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:00:37.416 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:00:37.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:00:37.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 08:00:37.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:00:37.423 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.81 ms, Average inference time: 8.27 ms

2025-08-01 08:00:37.425 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:00:37.503 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:00:37.583 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch488
2025-08-01 08:00:41.042 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 488/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.5, lr: 1.752e-04, size: 544, ETA: 0:41:46
2025-08-01 08:00:44.444 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 488/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 1.747e-04, size: 544, ETA: 0:41:42
2025-08-01 08:00:47.956 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 488/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 1.2, lr: 1.742e-04, size: 480, ETA: 0:41:39
2025-08-01 08:00:51.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 488/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, 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: 1.738e-04, size: 352, ETA: 0:41:35
2025-08-01 08:00:54.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 488/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.733e-04, size: 288, ETA: 0:41:32
2025-08-01 08:00:58.148 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 488/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.728e-04, size: 352, ETA: 0:41:28
2025-08-01 08:00:59.595 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:01:06.543 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:01:07.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:01:08.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4651
2025-08-01 08:01:08.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4000
2025-08-01 08:01:08.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2510
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3721
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.251
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.372
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:01:08.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:01:08.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:01:08.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:01:08.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:01:08.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:01:08.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:01:08.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:01:08.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:01:09.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:01:10.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:01:11.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:01:11.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:01:12.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:01:13.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:01:14.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:01:15.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:01:16.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:01:16.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:01:16.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 08:01:16.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:01:16.094 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.91 ms, Average inference time: 8.33 ms

2025-08-01 08:01:16.095 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:01:16.184 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:01:16.280 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch489
2025-08-01 08:01:19.467 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 489/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.9, lr: 1.722e-04, size: 288, ETA: 0:41:23
2025-08-01 08:01:22.636 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 489/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 1.717e-04, size: 448, ETA: 0:41:20
2025-08-01 08:01:25.792 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 489/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 1.712e-04, size: 352, ETA: 0:41:16
2025-08-01 08:01:29.090 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 489/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 1.708e-04, size: 512, ETA: 0:41:13
2025-08-01 08:01:32.487 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 489/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.005s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.7, lr: 1.703e-04, size: 480, ETA: 0:41:09
2025-08-01 08:01:35.770 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 489/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 1.698e-04, size: 384, ETA: 0:41:06
2025-08-01 08:01:37.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:01:43.927 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:01:44.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:01:45.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4108
2025-08-01 08:01:45.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3633
2025-08-01 08:01:45.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1596
2025-08-01 08:01:45.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3112
2025-08-01 08:01:45.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:01:45.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:01:45.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-01 08:01:45.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-01 08:01:45.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.160
2025-08-01 08:01:45.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.311
2025-08-01 08:01:45.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:01:45.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:01:45.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:01:45.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:01:45.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:01:45.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:01:45.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:01:45.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:01:45.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:01:45.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:01:46.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:01:47.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:01:47.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:01:48.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:01:48.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:01:49.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:01:49.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:01:50.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:01:50.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 08:01:50.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 08:01:50.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:01:50.586 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.82 ms, Average inference time: 8.38 ms

2025-08-01 08:01:50.589 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:01:50.665 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:01:50.747 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch490
2025-08-01 08:01:54.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 490/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.9, lr: 1.692e-04, size: 256, ETA: 0:41:01
2025-08-01 08:01:57.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 490/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.5, lr: 1.687e-04, size: 512, ETA: 0:40:57
2025-08-01 08:02:00.696 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 490/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 1.683e-04, size: 544, ETA: 0:40:54
2025-08-01 08:02:04.229 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 490/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.7, lr: 1.678e-04, size: 480, ETA: 0:40:50
2025-08-01 08:02:07.650 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 490/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, 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: 1.673e-04, size: 544, ETA: 0:40:47
2025-08-01 08:02:10.996 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 490/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 10.0, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 4.8, cls_loss: 0.7, lr: 1.669e-04, size: 576, ETA: 0:40:43
2025-08-01 08:02:12.581 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:02:19.256 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:02:20.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:02:20.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5029
2025-08-01 08:02:20.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4359
2025-08-01 08:02:20.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2583
2025-08-01 08:02:20.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3990
2025-08-01 08:02:20.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:02:20.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:02:20.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-01 08:02:20.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-08-01 08:02:20.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 08:02:20.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.399
2025-08-01 08:02:20.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:02:20.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:02:20.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:02:20.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:02:20.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:02:20.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:02:20.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:02:20.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:02:20.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:02:21.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:02:21.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:02:22.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:02:23.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:02:23.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:02:24.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:02:25.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:02:25.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:02:26.332 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:02:26.333 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:02:26.333 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:02:26.333 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:02:26.340 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.83 ms, Average inference time: 8.33 ms

2025-08-01 08:02:26.341 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:02:26.465 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:02:26.581 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch491
2025-08-01 08:02:29.735 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 491/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 1.8, cls_loss: 0.7, lr: 1.662e-04, size: 416, ETA: 0:40:38
2025-08-01 08:02:33.050 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 491/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.658e-04, size: 448, ETA: 0:40:35
2025-08-01 08:02:36.368 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 491/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 1.2, cls_loss: 0.6, lr: 1.653e-04, size: 320, ETA: 0:40:31
2025-08-01 08:02:39.715 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 491/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, 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: 1.649e-04, size: 384, ETA: 0:40:28
2025-08-01 08:02:42.964 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 491/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.644e-04, size: 320, ETA: 0:40:24
2025-08-01 08:02:46.379 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 491/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, 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: 1.640e-04, size: 544, ETA: 0:40:21
2025-08-01 08:02:47.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:02:54.644 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:02:55.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:02:55.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3334
2025-08-01 08:02:55.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2955
2025-08-01 08:02:55.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1337
2025-08-01 08:02:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2542
2025-08-01 08:02:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:02:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:02:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-01 08:02:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-01 08:02:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.134
2025-08-01 08:02:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.254
2025-08-01 08:02:55.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:02:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:02:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:02:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:02:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:02:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:02:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:02:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:02:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:02:56.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:02:57.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:02:57.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:02:58.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:02:58.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:02:59.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:02:59.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:03:00.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:03:01.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:03:01.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 08:03:01.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-08-01 08:03:01.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:03:01.027 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.88 ms, Average inference time: 8.41 ms

2025-08-01 08:03:01.028 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:03:01.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:03:01.226 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch492
2025-08-01 08:03:04.358 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 492/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.633e-04, size: 480, ETA: 0:40:16
2025-08-01 08:03:07.584 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 492/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.629e-04, size: 352, ETA: 0:40:12
2025-08-01 08:03:10.966 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 492/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.624e-04, size: 576, ETA: 0:40:09
2025-08-01 08:03:14.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 492/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.7, lr: 1.619e-04, size: 288, ETA: 0:40:05
2025-08-01 08:03:17.823 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 492/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.5, lr: 1.615e-04, size: 416, ETA: 0:40:02
2025-08-01 08:03:21.278 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 492/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 3.5, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 1.610e-04, size: 448, ETA: 0:39:59
2025-08-01 08:03:22.698 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:03:29.590 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:03:30.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:03:30.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4393
2025-08-01 08:03:30.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3441
2025-08-01 08:03:31.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2746
2025-08-01 08:03:31.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3527
2025-08-01 08:03:31.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:03:31.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:03:31.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 08:03:31.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-08-01 08:03:31.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-08-01 08:03:31.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-08-01 08:03:31.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:03:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:03:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:03:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:03:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:03:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:03:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:03:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:03:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:03:31.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:03:32.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:03:32.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:03:33.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:03:34.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:03:34.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:03:35.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:03:36.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:03:36.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:03:36.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:03:36.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 08:03:36.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:03:36.643 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.85 ms, Average inference time: 8.43 ms

2025-08-01 08:03:36.645 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:03:36.721 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:03:36.802 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch493
2025-08-01 08:03:39.901 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 493/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 1.604e-04, size: 288, ETA: 0:39:53
2025-08-01 08:03:43.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 493/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.599e-04, size: 448, ETA: 0:39:50
2025-08-01 08:03:46.590 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 493/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.595e-04, size: 480, ETA: 0:39:46
2025-08-01 08:03:50.021 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 493/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 3.7, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 1.591e-04, size: 576, ETA: 0:39:43
2025-08-01 08:03:53.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 493/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, 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: 1.586e-04, size: 448, ETA: 0:39:40
2025-08-01 08:03:57.052 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 493/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.582e-04, size: 480, ETA: 0:39:36
2025-08-01 08:03:58.610 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:04:05.411 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:04:06.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:04:06.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4449
2025-08-01 08:04:06.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4519
2025-08-01 08:04:06.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2305
2025-08-01 08:04:06.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3758
2025-08-01 08:04:06.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.231
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:04:06.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:04:06.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:04:06.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:04:06.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:04:06.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:04:07.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:04:07.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:04:08.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:04:08.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:04:09.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:04:10.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:04:10.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:04:11.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:04:11.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:04:11.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:04:11.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:04:11.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:04:11.678 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.83 ms, Average inference time: 8.28 ms

2025-08-01 08:04:11.679 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:04:11.793 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:04:11.907 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch494
2025-08-01 08:04:15.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 494/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.7, lr: 1.575e-04, size: 384, ETA: 0:39:31
2025-08-01 08:04:18.297 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 494/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 0.9, lr: 1.571e-04, size: 288, ETA: 0:39:28
2025-08-01 08:04:21.923 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 494/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 1.566e-04, size: 384, ETA: 0:39:24
2025-08-01 08:04:25.301 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 494/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.8, lr: 1.562e-04, size: 288, ETA: 0:39:21
2025-08-01 08:04:28.673 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 494/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 2.6, cls_loss: 0.8, lr: 1.557e-04, size: 544, ETA: 0:39:17
2025-08-01 08:04:32.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 494/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 1.553e-04, size: 544, ETA: 0:39:14
2025-08-01 08:04:33.542 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:04:40.541 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:04:41.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:04:41.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4873
2025-08-01 08:04:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4320
2025-08-01 08:04:42.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2130
2025-08-01 08:04:42.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3774
2025-08-01 08:04:42.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:04:42.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:04:42.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-01 08:04:42.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 08:04:42.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.377
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:04:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:04:42.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:04:43.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:04:43.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:04:44.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:04:45.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:04:45.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:04:46.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:04:47.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:04:47.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:04:47.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:04:47.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:04:47.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:04:47.754 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.86 ms, Average inference time: 8.43 ms

2025-08-01 08:04:47.755 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:04:47.923 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:04:48.024 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch495
2025-08-01 08:04:51.361 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 495/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.3, iou_loss: 1.4, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 0.9, lr: 1.547e-04, size: 416, ETA: 0:39:09
2025-08-01 08:04:54.855 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 495/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.7, lr: 1.542e-04, size: 416, ETA: 0:39:05
2025-08-01 08:04:58.174 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 495/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.538e-04, size: 544, ETA: 0:39:02
2025-08-01 08:05:01.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 495/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 3.4, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 1.533e-04, size: 416, ETA: 0:38:58
2025-08-01 08:05:05.019 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 495/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.529e-04, size: 384, ETA: 0:38:55
2025-08-01 08:05:08.195 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 495/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.525e-04, size: 288, ETA: 0:38:51
2025-08-01 08:05:09.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:05:16.450 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:05:17.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:05:17.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4129
2025-08-01 08:05:17.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3694
2025-08-01 08:05:17.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1565
2025-08-01 08:05:17.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3129
2025-08-01 08:05:17.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.156
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:05:17.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:05:17.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:05:17.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:05:18.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:05:18.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:05:19.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:05:19.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:05:19.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:05:20.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:05:20.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:05:21.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:05:21.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 08:05:21.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 08:05:21.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:05:21.253 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.78 ms, Average inference time: 8.26 ms

2025-08-01 08:05:21.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:05:21.371 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:05:21.456 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch496
2025-08-01 08:05:24.677 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 496/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 1.518e-04, size: 352, ETA: 0:38:46
2025-08-01 08:05:27.953 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 496/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.7, cls_loss: 0.6, lr: 1.514e-04, size: 256, ETA: 0:38:43
2025-08-01 08:05:31.410 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 496/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.9, lr: 1.510e-04, size: 288, ETA: 0:38:39
2025-08-01 08:05:34.756 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 496/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.9, lr: 1.505e-04, size: 320, ETA: 0:38:36
2025-08-01 08:05:37.967 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 496/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.501e-04, size: 416, ETA: 0:38:32
2025-08-01 08:05:41.505 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 496/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.496e-04, size: 352, ETA: 0:38:29
2025-08-01 08:05:42.989 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:05:49.985 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:05:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:05:51.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3692
2025-08-01 08:05:51.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3061
2025-08-01 08:05:51.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1601
2025-08-01 08:05:51.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2784
2025-08-01 08:05:51.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:05:51.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:05:51.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 08:05:51.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-08-01 08:05:51.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.160
2025-08-01 08:05:51.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.278
2025-08-01 08:05:51.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:05:51.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:05:51.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:05:51.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:05:51.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:05:51.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:05:51.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:05:51.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:05:51.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:05:52.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:05:52.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:05:53.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:05:53.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:05:54.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:05:55.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:05:55.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:05:56.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:05:57.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:05:57.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 08:05:57.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 08:05:57.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:05:57.081 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.87 ms, Average inference time: 8.43 ms

2025-08-01 08:05:57.082 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:05:57.160 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:05:57.288 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch497
2025-08-01 08:06:00.991 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 497/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.490e-04, size: 448, ETA: 0:38:24
2025-08-01 08:06:04.412 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 497/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, 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: 1.486e-04, size: 256, ETA: 0:38:20
2025-08-01 08:06:07.623 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 497/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.9, lr: 1.482e-04, size: 256, ETA: 0:38:17
2025-08-01 08:06:11.036 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 497/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 1.477e-04, size: 416, ETA: 0:38:13
2025-08-01 08:06:14.554 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 497/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.6, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 0.5, lr: 1.473e-04, size: 544, ETA: 0:38:10
2025-08-01 08:06:17.995 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 497/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.8, lr: 1.469e-04, size: 544, ETA: 0:38:07
2025-08-01 08:06:19.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:06:26.151 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:06:26.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:06:27.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4244
2025-08-01 08:06:27.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3590
2025-08-01 08:06:27.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1830
2025-08-01 08:06:27.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3221
2025-08-01 08:06:27.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:06:27.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:06:27.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.183
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:06:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:06:27.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:06:27.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:06:28.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:06:28.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:06:28.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:06:29.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:06:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:06:30.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:06:30.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:06:31.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:06:31.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 08:06:31.118 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 08:06:31.119 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:06:31.125 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.64 ms, Average NMS time: 0.82 ms, Average inference time: 8.46 ms

2025-08-01 08:06:31.127 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:06:31.211 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:06:31.289 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch498
2025-08-01 08:06:34.780 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 498/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, 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: 1.462e-04, size: 416, ETA: 0:38:02
2025-08-01 08:06:38.135 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 498/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 2.2, cls_loss: 0.5, lr: 1.458e-04, size: 544, ETA: 0:37:58
2025-08-01 08:06:41.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 498/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.454e-04, size: 256, ETA: 0:37:55
2025-08-01 08:06:44.720 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 498/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.5, lr: 1.449e-04, size: 288, ETA: 0:37:51
2025-08-01 08:06:48.381 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 498/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.445e-04, size: 320, ETA: 0:37:48
2025-08-01 08:06:51.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 498/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.441e-04, size: 288, ETA: 0:37:44
2025-08-01 08:06:53.139 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:06:59.908 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:07:00.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:07:01.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4572
2025-08-01 08:07:01.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3436
2025-08-01 08:07:01.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2233
2025-08-01 08:07:01.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3414
2025-08-01 08:07:01.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:07:01.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.341
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:07:01.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:07:01.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:07:01.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:07:01.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:07:02.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:07:03.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:07:03.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:07:04.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:07:05.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:07:06.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:07:06.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:07:07.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:07:08.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:07:08.576 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:07:08.576 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 08:07:08.576 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:07:08.586 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.90 ms, Average inference time: 8.47 ms

2025-08-01 08:07:08.587 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:07:08.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:07:08.858 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch499
2025-08-01 08:07:12.105 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 499/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.8, cls_loss: 0.7, lr: 1.435e-04, size: 448, ETA: 0:37:39
2025-08-01 08:07:15.375 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 499/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.430e-04, size: 320, ETA: 0:37:36
2025-08-01 08:07:18.558 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 499/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 1.426e-04, size: 352, ETA: 0:37:32
2025-08-01 08:07:21.919 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 499/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.6, lr: 1.422e-04, size: 576, ETA: 0:37:29
2025-08-01 08:07:25.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 499/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.5, lr: 1.418e-04, size: 384, ETA: 0:37:25
2025-08-01 08:07:29.285 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 499/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.186s, data_time: 0.001s, total_loss: 6.2, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 6.2, cls_loss: 0.0, lr: 1.414e-04, size: 352, ETA: 0:37:22
2025-08-01 08:07:30.841 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:07:37.903 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:07:38.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:07:38.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3472
2025-08-01 08:07:39.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2202
2025-08-01 08:07:39.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1528
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2401
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.220
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.240
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:07:39.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:07:39.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:07:39.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:07:39.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:07:39.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:07:39.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:07:39.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:07:39.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:07:39.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:07:40.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:07:40.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:07:41.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:07:41.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:07:42.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:07:42.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:07:43.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:07:43.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:07:43.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 08:07:43.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 08:07:43.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:07:43.594 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.87 ms, Average inference time: 8.44 ms

2025-08-01 08:07:43.595 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:07:43.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:07:43.846 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch500
2025-08-01 08:07:47.158 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 500/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.5, lr: 1.407e-04, size: 480, ETA: 0:37:17
2025-08-01 08:07:50.550 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 500/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.403e-04, size: 480, ETA: 0:37:13
2025-08-01 08:07:54.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 500/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 3.4, cls_loss: 0.6, lr: 1.399e-04, size: 576, ETA: 0:37:10
2025-08-01 08:07:57.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 500/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.6, lr: 1.395e-04, size: 576, ETA: 0:37:07
2025-08-01 08:08:01.211 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 500/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 1.2, lr: 1.391e-04, size: 480, ETA: 0:37:03
2025-08-01 08:08:04.670 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 500/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.386e-04, size: 512, ETA: 0:37:00
2025-08-01 08:08:06.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:08:13.042 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:08:13.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:08:14.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5123
2025-08-01 08:08:14.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4858
2025-08-01 08:08:14.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2848
2025-08-01 08:08:14.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4276
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:08:14.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:08:14.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:08:14.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:08:14.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:08:14.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:08:14.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:08:14.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:08:15.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:08:15.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:08:16.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:08:17.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:08:17.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:08:18.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:08:18.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:08:19.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:08:20.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:08:20.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 08:08:20.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 08:08:20.325 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:08:20.332 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.89 ms, Average inference time: 8.34 ms

2025-08-01 08:08:20.335 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:08:20.410 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:08:20.490 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch501
2025-08-01 08:08:23.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 501/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.380e-04, size: 448, ETA: 0:36:55
2025-08-01 08:08:27.250 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 501/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.8, lr: 1.376e-04, size: 576, ETA: 0:36:51
2025-08-01 08:08:30.739 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 501/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.7, lr: 1.372e-04, size: 256, ETA: 0:36:48
2025-08-01 08:08:34.437 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 501/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 0.7, lr: 1.368e-04, size: 448, ETA: 0:36:44
2025-08-01 08:08:37.870 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 501/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 1.364e-04, size: 256, ETA: 0:36:41
2025-08-01 08:08:41.234 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 501/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.359e-04, size: 416, ETA: 0:36:37
2025-08-01 08:08:42.706 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:08:49.341 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:08:50.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:08:50.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4985
2025-08-01 08:08:50.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3874
2025-08-01 08:08:50.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3032
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3963
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:08:50.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:08:50.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:08:50.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:08:50.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:08:50.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:08:50.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:08:50.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:08:50.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:08:51.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:08:52.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:08:52.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:08:53.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:08:53.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:08:54.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:08:55.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:08:55.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:08:56.438 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:08:56.439 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:08:56.439 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:08:56.439 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:08:56.446 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.88 ms, Average inference time: 8.43 ms

2025-08-01 08:08:56.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:08:56.530 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:08:56.608 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch502
2025-08-01 08:08:59.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 502/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.353e-04, size: 320, ETA: 0:36:32
2025-08-01 08:09:03.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 502/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.1, cls_loss: 0.6, lr: 1.349e-04, size: 416, ETA: 0:36:29
2025-08-01 08:09:06.674 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 502/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.345e-04, size: 288, ETA: 0:36:25
2025-08-01 08:09:09.950 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 502/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 1.341e-04, size: 384, ETA: 0:36:22
2025-08-01 08:09:13.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 502/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.337e-04, size: 448, ETA: 0:36:19
2025-08-01 08:09:16.776 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 502/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 1.0, lr: 1.333e-04, size: 384, ETA: 0:36:15
2025-08-01 08:09:18.273 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:09:24.977 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:09:25.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:09:25.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4630
2025-08-01 08:09:26.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3382
2025-08-01 08:09:26.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2426
2025-08-01 08:09:26.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3479
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.243
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.348
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:09:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:09:26.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:09:26.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:09:26.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:09:26.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:09:26.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:09:26.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:09:27.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:09:27.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:09:28.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:09:28.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:09:29.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:09:29.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:09:30.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:09:30.637 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:09:30.637 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:09:30.637 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 08:09:30.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:09:30.644 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.82 ms, Average inference time: 8.25 ms

2025-08-01 08:09:30.645 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:09:30.725 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:09:30.805 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch503
2025-08-01 08:09:33.914 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 503/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 1.327e-04, size: 288, ETA: 0:36:10
2025-08-01 08:09:37.251 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 503/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 14.3, iou_loss: 4.2, l1_loss: 1.6, conf_loss: 7.7, cls_loss: 0.9, lr: 1.323e-04, size: 320, ETA: 0:36:07
2025-08-01 08:09:40.484 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 503/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 1.319e-04, size: 448, ETA: 0:36:03
2025-08-01 08:09:43.846 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 503/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.314e-04, size: 352, ETA: 0:36:00
2025-08-01 08:09:47.372 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 503/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, 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: 1.310e-04, size: 544, ETA: 0:35:56
2025-08-01 08:09:50.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 503/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 1.306e-04, size: 320, ETA: 0:35:53
2025-08-01 08:09:52.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:09:58.828 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:09:59.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:09:59.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4243
2025-08-01 08:09:59.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3735
2025-08-01 08:10:00.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2135
2025-08-01 08:10:00.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3371
2025-08-01 08:10:00.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:10:00.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:10:00.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 08:10:00.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-01 08:10:00.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.214
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:10:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:10:00.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:10:00.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:10:01.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:10:01.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:10:02.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:10:02.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:10:03.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:10:03.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:10:04.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:10:04.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 08:10:04.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 08:10:04.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:10:04.317 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.84 ms, Average inference time: 8.30 ms

2025-08-01 08:10:04.318 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:10:04.401 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:10:04.478 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch504
2025-08-01 08:10:07.836 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 504/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.300e-04, size: 384, ETA: 0:35:48
2025-08-01 08:10:11.264 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 504/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.5, lr: 1.296e-04, size: 416, ETA: 0:35:44
2025-08-01 08:10:14.519 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 504/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.5, lr: 1.292e-04, size: 384, ETA: 0:35:41
2025-08-01 08:10:17.824 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 504/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.288e-04, size: 480, ETA: 0:35:37
2025-08-01 08:10:21.153 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 504/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, 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: 1.284e-04, size: 320, ETA: 0:35:34
2025-08-01 08:10:24.446 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 504/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.8, lr: 1.280e-04, size: 384, ETA: 0:35:30
2025-08-01 08:10:25.858 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:10:32.778 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:10:34.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:10:34.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3632
2025-08-01 08:10:34.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3079
2025-08-01 08:10:34.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1570
2025-08-01 08:10:34.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2760
2025-08-01 08:10:34.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:10:34.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:10:34.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-01 08:10:34.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-08-01 08:10:34.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.157
2025-08-01 08:10:34.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.276
2025-08-01 08:10:34.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:10:34.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:10:34.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:10:34.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:10:34.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:10:34.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:10:34.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:10:34.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:10:34.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:10:35.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:10:36.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:10:37.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:10:38.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:10:38.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:10:39.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:10:40.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:10:41.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:10:42.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:10:42.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:10:42.043 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 08:10:42.043 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:10:42.050 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.90 ms, Average inference time: 8.35 ms

2025-08-01 08:10:42.067 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:10:42.144 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:10:42.224 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch505
2025-08-01 08:10:45.372 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 505/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.274e-04, size: 448, ETA: 0:35:25
2025-08-01 08:10:48.708 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 505/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 1.270e-04, size: 480, ETA: 0:35:22
2025-08-01 08:10:52.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 505/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.266e-04, size: 320, ETA: 0:35:18
2025-08-01 08:10:55.340 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 505/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.7, lr: 1.262e-04, size: 448, ETA: 0:35:15
2025-08-01 08:10:58.694 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 505/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 1.258e-04, size: 480, ETA: 0:35:11
2025-08-01 08:11:02.008 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 505/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.254e-04, size: 480, ETA: 0:35:08
2025-08-01 08:11:03.567 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:11:10.446 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:11:11.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:11:11.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4660
2025-08-01 08:11:11.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3568
2025-08-01 08:11:11.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2161
2025-08-01 08:11:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3463
2025-08-01 08:11:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:11:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:11:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-01 08:11:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-08-01 08:11:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-08-01 08:11:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.346
2025-08-01 08:11:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:11:11.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:11:11.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:11:11.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:11:11.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:11:11.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:11:11.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:11:11.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:11:11.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:11:12.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:11:13.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:11:13.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:11:14.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:11:14.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:11:15.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:11:15.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:11:16.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:11:16.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:11:16.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 08:11:16.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 08:11:16.950 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:11:16.957 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.68 ms, Average NMS time: 0.87 ms, Average inference time: 8.55 ms

2025-08-01 08:11:16.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:11:17.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:11:17.115 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch506
2025-08-01 08:11:20.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 506/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.5, lr: 1.248e-04, size: 288, ETA: 0:35:03
2025-08-01 08:11:23.855 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 506/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 4.3, cls_loss: 1.2, lr: 1.244e-04, size: 448, ETA: 0:34:59
2025-08-01 08:11:27.273 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 506/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.240e-04, size: 256, ETA: 0:34:56
2025-08-01 08:11:30.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 506/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.6, lr: 1.236e-04, size: 576, ETA: 0:34:52
2025-08-01 08:11:34.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 506/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.232e-04, size: 512, ETA: 0:34:49
2025-08-01 08:11:37.428 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 506/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.228e-04, size: 288, ETA: 0:34:45
2025-08-01 08:11:38.887 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:11:45.616 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:11:46.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:11:46.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4509
2025-08-01 08:11:46.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3671
2025-08-01 08:11:46.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2692
2025-08-01 08:11:46.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3624
2025-08-01 08:11:46.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:11:46.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:11:46.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-01 08:11:46.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.362
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:11:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:11:46.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:11:47.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:11:47.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:11:48.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:11:49.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:11:49.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:11:50.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:11:50.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:11:51.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:11:51.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:11:51.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:11:51.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 08:11:51.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:11:51.860 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.86 ms, Average inference time: 8.39 ms

2025-08-01 08:11:51.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:11:51.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:11:52.074 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch507
2025-08-01 08:11:55.420 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 507/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.223e-04, size: 576, ETA: 0:34:40
2025-08-01 08:11:59.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 507/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.219e-04, size: 544, ETA: 0:34:37
2025-08-01 08:12:02.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 507/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, 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: 1.215e-04, size: 320, ETA: 0:34:34
2025-08-01 08:12:05.983 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 507/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.3, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 2.4, cls_loss: 1.5, lr: 1.211e-04, size: 256, ETA: 0:34:30
2025-08-01 08:12:09.287 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 507/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.207e-04, size: 416, ETA: 0:34:27
2025-08-01 08:12:13.077 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 507/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.188s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 1.203e-04, size: 384, ETA: 0:34:23
2025-08-01 08:12:14.632 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:12:21.520 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:12:22.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:12:22.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5025
2025-08-01 08:12:22.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4308
2025-08-01 08:12:22.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3069
2025-08-01 08:12:22.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4134
2025-08-01 08:12:22.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.413
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:12:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:12:22.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:12:22.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:12:22.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:12:23.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:12:23.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:12:24.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:12:25.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:12:25.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:12:26.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:12:26.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:12:27.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:12:27.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:12:27.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:12:27.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 08:12:27.822 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:12:27.831 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.84 ms, Average inference time: 8.36 ms

2025-08-01 08:12:27.832 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:12:27.955 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:12:28.073 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch508
2025-08-01 08:12:31.474 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 508/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.197e-04, size: 448, ETA: 0:34:18
2025-08-01 08:12:34.693 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 508/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 1.193e-04, size: 288, ETA: 0:34:15
2025-08-01 08:12:38.085 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 508/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.190e-04, size: 512, ETA: 0:34:11
2025-08-01 08:12:41.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 508/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 1.186e-04, size: 288, ETA: 0:34:08
2025-08-01 08:12:45.212 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 508/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 1.182e-04, size: 512, ETA: 0:34:04
2025-08-01 08:12:48.621 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 508/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.7, lr: 1.178e-04, size: 288, ETA: 0:34:01
2025-08-01 08:12:50.001 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:12:56.718 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:12:57.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:12:58.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4621
2025-08-01 08:12:58.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4267
2025-08-01 08:12:58.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2508
2025-08-01 08:12:58.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3799
2025-08-01 08:12:58.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:12:58.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:12:58.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-01 08:12:58.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-08-01 08:12:58.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.251
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:12:58.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:12:58.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:12:59.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:13:00.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:13:00.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:13:01.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:13:02.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:13:02.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:13:03.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:13:03.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:13:03.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:13:03.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:13:03.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:13:03.987 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.87 ms, Average inference time: 8.42 ms

2025-08-01 08:13:03.988 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:13:04.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:13:04.152 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch509
2025-08-01 08:13:07.368 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 509/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.172e-04, size: 544, ETA: 0:33:56
2025-08-01 08:13:10.627 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 509/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 1.168e-04, size: 384, ETA: 0:33:52
2025-08-01 08:13:13.824 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 509/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.152s, 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: 1.164e-04, size: 384, ETA: 0:33:49
2025-08-01 08:13:17.362 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 509/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.161e-04, size: 480, ETA: 0:33:46
2025-08-01 08:13:20.585 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 509/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.157e-04, size: 416, ETA: 0:33:42
2025-08-01 08:13:24.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 509/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.153e-04, size: 576, ETA: 0:33:39
2025-08-01 08:13:25.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:13:32.378 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:13:33.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:13:33.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5038
2025-08-01 08:13:33.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4611
2025-08-01 08:13:33.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2403
2025-08-01 08:13:33.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4017
2025-08-01 08:13:33.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:13:33.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:13:33.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-01 08:13:33.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-08-01 08:13:33.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.402
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:13:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:13:34.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:13:35.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:13:35.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:13:36.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:13:36.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:13:37.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:13:38.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:13:38.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:13:39.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:13:39.481 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:13:39.481 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:13:39.481 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:13:39.487 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.83 ms, Average inference time: 8.30 ms

2025-08-01 08:13:39.489 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:13:39.573 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:13:39.657 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch510
2025-08-01 08:13:42.765 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 510/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 9.2, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 4.4, cls_loss: 0.9, lr: 1.147e-04, size: 416, ETA: 0:33:34
2025-08-01 08:13:46.004 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 510/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 1.0, lr: 1.143e-04, size: 576, ETA: 0:33:30
2025-08-01 08:13:49.654 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 510/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.140e-04, size: 256, ETA: 0:33:27
2025-08-01 08:13:52.968 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 510/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 1.136e-04, size: 384, ETA: 0:33:23
2025-08-01 08:13:56.254 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 510/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 1.132e-04, size: 576, ETA: 0:33:20
2025-08-01 08:13:59.946 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 510/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.5, lr: 1.128e-04, size: 320, ETA: 0:33:16
2025-08-01 08:14:01.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:14:08.425 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:14:09.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:14:10.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4714
2025-08-01 08:14:10.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4368
2025-08-01 08:14:10.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2703
2025-08-01 08:14:10.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3928
2025-08-01 08:14:10.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.393
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:14:10.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:14:10.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:14:10.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:14:11.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:14:13.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:14:14.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:14:15.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:14:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:14:17.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:14:18.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:14:20.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:14:21.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:14:21.289 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:14:21.289 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 08:14:21.289 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:14:21.296 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.91 ms, Average inference time: 8.42 ms

2025-08-01 08:14:21.298 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:14:21.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:14:21.496 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch511
2025-08-01 08:14:24.621 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 511/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.123e-04, size: 416, ETA: 0:33:11
2025-08-01 08:14:28.140 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 511/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 1.119e-04, size: 416, ETA: 0:33:08
2025-08-01 08:14:31.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 511/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.4, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.6, lr: 1.115e-04, size: 576, ETA: 0:33:04
2025-08-01 08:14:35.082 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 511/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 9.9, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 4.8, cls_loss: 0.9, lr: 1.111e-04, size: 256, ETA: 0:33:01
2025-08-01 08:14:38.726 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 511/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 1.108e-04, size: 576, ETA: 0:32:58
2025-08-01 08:14:42.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 511/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 1.104e-04, size: 512, ETA: 0:32:54
2025-08-01 08:14:43.577 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:14:50.490 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:14:51.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:14:51.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5274
2025-08-01 08:14:51.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4811
2025-08-01 08:14:51.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2855
2025-08-01 08:14:51.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4314
2025-08-01 08:14:51.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:14:51.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:14:51.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:14:52.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:14:53.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:14:53.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:14:54.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:14:55.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:14:55.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:14:56.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:14:57.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:14:57.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:14:57.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 08:14:57.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-01 08:14:57.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:14:57.718 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.84 ms, Average inference time: 8.31 ms

2025-08-01 08:14:57.719 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:14:57.797 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:14:57.892 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch512
2025-08-01 08:15:01.132 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 512/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.098e-04, size: 480, ETA: 0:32:49
2025-08-01 08:15:04.285 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 512/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 0.7, lr: 1.095e-04, size: 352, ETA: 0:32:46
2025-08-01 08:15:07.742 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 512/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.5, lr: 1.091e-04, size: 320, ETA: 0:32:42
2025-08-01 08:15:11.111 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 512/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.087e-04, size: 320, ETA: 0:32:39
2025-08-01 08:15:14.632 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 512/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.8, lr: 1.083e-04, size: 448, ETA: 0:32:35
2025-08-01 08:15:17.984 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 512/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 1.080e-04, size: 320, ETA: 0:32:32
2025-08-01 08:15:19.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:15:26.380 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:15:26.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:15:27.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4554
2025-08-01 08:15:27.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3956
2025-08-01 08:15:27.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2642
2025-08-01 08:15:27.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3718
2025-08-01 08:15:27.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:15:27.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:15:27.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.372
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:15:27.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:15:27.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:15:27.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:15:27.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:15:27.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:15:28.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:15:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:15:29.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:15:29.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:15:29.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:15:30.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:15:30.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:15:31.255 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:15:31.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:15:31.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 08:15:31.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:15:31.265 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.86 ms, Average inference time: 8.37 ms

2025-08-01 08:15:31.265 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:15:31.436 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:15:31.518 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch513
2025-08-01 08:15:34.599 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 513/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 1.074e-04, size: 352, ETA: 0:32:27
2025-08-01 08:15:37.826 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 513/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 2.1, cls_loss: 0.7, lr: 1.070e-04, size: 512, ETA: 0:32:23
2025-08-01 08:15:41.189 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 513/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 14.7, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 14.7, cls_loss: 0.0, lr: 1.067e-04, size: 544, ETA: 0:32:20
2025-08-01 08:15:44.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 513/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.7, lr: 1.063e-04, size: 256, ETA: 0:32:16
2025-08-01 08:15:47.804 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 513/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, 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: 1.059e-04, size: 320, ETA: 0:32:13
2025-08-01 08:15:51.020 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 513/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 1.056e-04, size: 416, ETA: 0:32:09
2025-08-01 08:15:52.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:15:59.191 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:15:59.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:15:59.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4728
2025-08-01 08:16:00.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4149
2025-08-01 08:16:00.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2593
2025-08-01 08:16:00.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3823
2025-08-01 08:16:00.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:16:00.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:16:00.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-08-01 08:16:00.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-01 08:16:00.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.259
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:16:00.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:16:00.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:16:00.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:16:01.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:16:01.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:16:01.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:16:02.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:16:02.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:16:03.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:16:03.716 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:16:03.716 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:16:03.717 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:16:03.717 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:16:03.727 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.77 ms, Average inference time: 8.31 ms

2025-08-01 08:16:03.728 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:16:03.846 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:16:03.944 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch514
2025-08-01 08:16:07.242 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 514/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.050e-04, size: 352, ETA: 0:32:04
2025-08-01 08:16:10.592 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 514/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.6, lr: 1.047e-04, size: 256, ETA: 0:32:01
2025-08-01 08:16:13.953 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 514/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.0, cls_loss: 0.6, lr: 1.043e-04, size: 352, ETA: 0:31:57
2025-08-01 08:16:17.080 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 514/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 1.039e-04, size: 256, ETA: 0:31:54
2025-08-01 08:16:20.388 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 514/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.0, cls_loss: 0.6, lr: 1.036e-04, size: 384, ETA: 0:31:50
2025-08-01 08:16:23.691 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 514/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 1.032e-04, size: 416, ETA: 0:31:47
2025-08-01 08:16:25.196 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:16:32.039 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:16:32.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:16:32.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5088
2025-08-01 08:16:32.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4495
2025-08-01 08:16:32.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2726
2025-08-01 08:16:32.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4103
2025-08-01 08:16:32.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:16:32.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:16:32.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-01 08:16:32.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-08-01 08:16:32.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.273
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.410
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:16:32.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:16:33.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:16:33.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:16:34.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:16:34.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:16:35.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:16:35.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:16:35.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:16:36.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:16:36.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:16:36.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:16:36.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 08:16:36.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:16:36.760 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.63 ms, Average NMS time: 0.80 ms, Average inference time: 8.44 ms

2025-08-01 08:16:36.761 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:16:36.857 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:16:37.019 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch515
2025-08-01 08:16:40.379 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 515/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.027e-04, size: 544, ETA: 0:31:42
2025-08-01 08:16:43.794 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 515/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 1.023e-04, size: 512, ETA: 0:31:38
2025-08-01 08:16:47.302 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 515/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.019e-04, size: 544, ETA: 0:31:35
2025-08-01 08:16:50.705 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 515/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 1.016e-04, size: 352, ETA: 0:31:32
2025-08-01 08:16:53.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 515/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.012e-04, size: 416, ETA: 0:31:28
2025-08-01 08:16:57.449 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 515/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.6, lr: 1.009e-04, size: 416, ETA: 0:31:25
2025-08-01 08:16:58.948 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:17:05.763 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:17:06.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:17:07.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3781
2025-08-01 08:17:07.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3109
2025-08-01 08:17:07.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0994
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2628
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.099
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.263
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:17:07.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:17:07.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:17:07.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:17:07.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:17:07.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:17:07.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:17:07.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:17:08.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:17:09.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:17:09.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:17:10.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:17:11.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:17:11.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:17:12.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:17:13.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:17:13.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:17:13.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 08:17:13.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 08:17:13.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:17:13.970 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.87 ms, Average inference time: 8.37 ms

2025-08-01 08:17:13.972 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:17:14.048 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:17:14.153 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch516
2025-08-01 08:17:17.803 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 516/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.003e-04, size: 512, ETA: 0:31:20
2025-08-01 08:17:21.179 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 516/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 1.0, lr: 9.997e-05, size: 288, ETA: 0:31:16
2025-08-01 08:17:24.790 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 516/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 9.961e-05, size: 544, ETA: 0:31:13
2025-08-01 08:17:28.196 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 516/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.8, lr: 9.925e-05, size: 288, ETA: 0:31:09
2025-08-01 08:17:31.577 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 516/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 2.2, cls_loss: 0.6, lr: 9.889e-05, size: 320, ETA: 0:31:06
2025-08-01 08:17:35.211 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 516/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 9.853e-05, size: 544, ETA: 0:31:02
2025-08-01 08:17:36.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:17:43.492 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:17:44.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:17:44.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4070
2025-08-01 08:17:44.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3044
2025-08-01 08:17:44.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1811
2025-08-01 08:17:44.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2975
2025-08-01 08:17:44.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:17:44.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:17:44.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-01 08:17:44.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-01 08:17:44.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-08-01 08:17:44.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.298
2025-08-01 08:17:44.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:17:44.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:17:44.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:17:44.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:17:44.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:17:44.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:17:44.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:17:44.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:17:44.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:17:45.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:17:45.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:17:46.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:17:46.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:17:47.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:17:47.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:17:48.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:17:48.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:17:49.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:17:49.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 08:17:49.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 08:17:49.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:17:49.413 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.84 ms, Average inference time: 8.42 ms

2025-08-01 08:17:49.414 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:17:49.498 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:17:49.580 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch517
2025-08-01 08:17:53.170 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 517/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.802e-05, size: 256, ETA: 0:30:58
2025-08-01 08:17:56.546 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 517/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 9.766e-05, size: 480, ETA: 0:30:54
2025-08-01 08:17:59.956 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 517/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 9.730e-05, size: 416, ETA: 0:30:51
2025-08-01 08:18:03.370 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 517/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 9.695e-05, size: 256, ETA: 0:30:47
2025-08-01 08:18:06.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 517/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 9.659e-05, size: 576, ETA: 0:30:44
2025-08-01 08:18:10.194 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 517/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 9.624e-05, size: 256, ETA: 0:30:40
2025-08-01 08:18:11.777 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:18:18.594 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:18:19.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:18:19.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4545
2025-08-01 08:18:19.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3966
2025-08-01 08:18:19.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2253
2025-08-01 08:18:19.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3588
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.225
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:18:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:18:19.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:18:19.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:18:19.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:18:19.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:18:20.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:18:20.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:18:21.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:18:21.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:18:22.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:18:22.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:18:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:18:23.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:18:24.482 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:18:24.482 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 08:18:24.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 08:18:24.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:18:24.492 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.86 ms, Average inference time: 8.35 ms

2025-08-01 08:18:24.493 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:18:24.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:18:24.759 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch518
2025-08-01 08:18:28.129 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 518/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.7, lr: 9.573e-05, size: 448, ETA: 0:30:35
2025-08-01 08:18:31.659 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 518/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.5, lr: 9.538e-05, size: 416, ETA: 0:30:32
2025-08-01 08:18:35.115 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 518/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.8, lr: 9.503e-05, size: 512, ETA: 0:30:28
2025-08-01 08:18:38.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 518/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 9.467e-05, size: 448, ETA: 0:30:25
2025-08-01 08:18:42.297 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 518/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.9, lr: 9.432e-05, size: 384, ETA: 0:30:22
2025-08-01 08:18:45.632 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 518/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 1.0, lr: 9.397e-05, size: 288, ETA: 0:30:18
2025-08-01 08:18:47.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:18:54.061 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:18:54.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:18:55.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3674
2025-08-01 08:18:55.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2582
2025-08-01 08:18:55.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1616
2025-08-01 08:18:55.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2624
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.162
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.262
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:18:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:18:55.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:18:55.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:18:55.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:18:55.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:18:56.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:18:57.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:18:57.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:18:58.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:18:59.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:18:59.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:19:00.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:19:01.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:19:02.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:19:02.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 08:19:02.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-08-01 08:19:02.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:19:02.023 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.89 ms, Average inference time: 8.33 ms

2025-08-01 08:19:02.024 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:19:02.151 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:19:02.233 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch519
2025-08-01 08:19:05.659 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 519/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 9.347e-05, size: 480, ETA: 0:30:13
2025-08-01 08:19:09.129 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 519/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 9.312e-05, size: 288, ETA: 0:30:10
2025-08-01 08:19:12.432 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 519/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 9.277e-05, size: 352, ETA: 0:30:06
2025-08-01 08:19:16.028 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 519/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.178s, 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: 9.243e-05, size: 480, ETA: 0:30:03
2025-08-01 08:19:19.710 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 519/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 3.7, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.5, lr: 9.208e-05, size: 288, ETA: 0:29:59
2025-08-01 08:19:22.974 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 519/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 9.173e-05, size: 320, ETA: 0:29:56
2025-08-01 08:19:24.424 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:19:31.167 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:19:32.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:19:33.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4841
2025-08-01 08:19:33.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3686
2025-08-01 08:19:33.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2765
2025-08-01 08:19:33.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3764
2025-08-01 08:19:33.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.277
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:19:33.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:19:33.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:19:33.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:19:34.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:19:35.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:19:36.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:19:37.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:19:38.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:19:38.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:19:39.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:19:40.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:19:41.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:19:41.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:19:41.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:19:41.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:19:41.732 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.90 ms, Average inference time: 8.41 ms

2025-08-01 08:19:41.733 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:19:41.809 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:19:41.891 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch520
2025-08-01 08:19:45.000 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 520/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 9.123e-05, size: 576, ETA: 0:29:51
2025-08-01 08:19:48.417 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 520/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.6, lr: 9.089e-05, size: 320, ETA: 0:29:47
2025-08-01 08:19:51.823 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 520/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 9.055e-05, size: 288, ETA: 0:29:44
2025-08-01 08:19:55.058 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 520/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.5, lr: 9.020e-05, size: 480, ETA: 0:29:40
2025-08-01 08:19:58.483 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 520/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 8.986e-05, size: 384, ETA: 0:29:37
2025-08-01 08:20:01.723 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 520/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 8.952e-05, size: 512, ETA: 0:29:34
2025-08-01 08:20:03.301 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:20:09.991 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:20:10.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:20:10.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4836
2025-08-01 08:20:11.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4925
2025-08-01 08:20:11.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2415
2025-08-01 08:20:11.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4059
2025-08-01 08:20:11.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:20:11.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:20:11.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.241
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.406
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:20:11.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:20:11.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:20:11.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:20:11.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:20:12.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:20:12.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:20:13.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:20:13.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:20:14.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:20:14.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:20:15.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:20:15.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:20:15.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 08:20:15.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:20:15.157 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.64 ms, Average NMS time: 0.84 ms, Average inference time: 8.47 ms

2025-08-01 08:20:15.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:20:15.291 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:20:15.373 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch521
2025-08-01 08:20:18.625 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 521/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 8.902e-05, size: 320, ETA: 0:29:28
2025-08-01 08:20:21.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 521/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 8.868e-05, size: 512, ETA: 0:29:25
2025-08-01 08:20:25.376 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 521/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 8.834e-05, size: 384, ETA: 0:29:22
2025-08-01 08:20:28.674 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 521/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, 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: 8.801e-05, size: 416, ETA: 0:29:18
2025-08-01 08:20:31.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 521/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 8.767e-05, size: 384, ETA: 0:29:15
2025-08-01 08:20:35.300 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 521/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 8.733e-05, size: 512, ETA: 0:29:11
2025-08-01 08:20:36.768 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:20:43.605 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:20:44.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:20:44.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3360
2025-08-01 08:20:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3313
2025-08-01 08:20:45.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1530
2025-08-01 08:20:45.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2734
2025-08-01 08:20:45.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:20:45.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:20:45.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.273
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:20:45.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:20:45.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:20:45.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:20:45.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:20:46.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:20:46.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:20:47.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:20:47.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:20:48.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:20:49.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:20:49.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:20:50.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:20:50.421 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-08-01 08:20:50.421 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 08:20:50.421 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:20:50.431 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.89 ms, Average inference time: 8.36 ms

2025-08-01 08:20:50.432 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:20:50.524 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:20:50.612 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch522
2025-08-01 08:20:53.840 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 522/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.8, lr: 8.684e-05, size: 288, ETA: 0:29:06
2025-08-01 08:20:57.231 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 522/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 8.650e-05, size: 320, ETA: 0:29:03
2025-08-01 08:21:00.535 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 522/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 8.617e-05, size: 256, ETA: 0:28:59
2025-08-01 08:21:04.145 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 522/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 8.583e-05, size: 544, ETA: 0:28:56
2025-08-01 08:21:07.474 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 522/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 8.550e-05, size: 416, ETA: 0:28:52
2025-08-01 08:21:10.699 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 522/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 8.517e-05, size: 256, ETA: 0:28:49
2025-08-01 08:21:12.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:21:19.163 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:21:19.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:21:20.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5098
2025-08-01 08:21:20.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4502
2025-08-01 08:21:20.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3144
2025-08-01 08:21:20.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4248
2025-08-01 08:21:20.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:21:20.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:21:20.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-01 08:21:20.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.425
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:21:20.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:21:20.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:21:21.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:21:21.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:21:22.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:21:22.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:21:23.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:21:23.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:21:24.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:21:24.871 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:21:24.872 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-01 08:21:24.872 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 08:21:24.872 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:21:24.879 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.84 ms, Average inference time: 8.26 ms

2025-08-01 08:21:24.880 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:21:24.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:21:25.043 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch523
2025-08-01 08:21:28.463 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 523/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.7, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.5, lr: 8.468e-05, size: 576, ETA: 0:28:44
2025-08-01 08:21:31.916 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 523/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.8, lr: 8.435e-05, size: 544, ETA: 0:28:40
2025-08-01 08:21:35.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 523/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, 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: 8.402e-05, size: 256, ETA: 0:28:37
2025-08-01 08:21:38.789 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 523/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.178s, data_time: 0.006s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 8.369e-05, size: 576, ETA: 0:28:34
2025-08-01 08:21:42.291 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 523/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, 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: 8.336e-05, size: 384, ETA: 0:28:30
2025-08-01 08:21:45.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 523/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.6, lr: 8.303e-05, size: 416, ETA: 0:28:27
2025-08-01 08:21:47.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:21:54.140 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:21:55.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:21:55.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4295
2025-08-01 08:21:55.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3157
2025-08-01 08:21:55.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1899
2025-08-01 08:21:55.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3117
2025-08-01 08:21:55.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:21:55.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:21:55.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-01 08:21:55.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-01 08:21:55.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.190
2025-08-01 08:21:55.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.312
2025-08-01 08:21:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:21:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:21:55.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:21:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:21:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:21:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:21:55.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:21:55.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:21:55.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:21:56.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:21:57.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:21:57.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:21:58.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:21:59.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:21:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:22:00.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:22:01.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:22:02.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:22:02.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 08:22:02.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 08:22:02.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:22:02.038 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.84 ms, Average inference time: 8.38 ms

2025-08-01 08:22:02.040 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:22:02.159 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:22:02.245 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch524
2025-08-01 08:22:05.601 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 524/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, 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: 8.255e-05, size: 576, ETA: 0:28:22
2025-08-01 08:22:08.911 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 524/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.6, lr: 8.222e-05, size: 352, ETA: 0:28:18
2025-08-01 08:22:12.197 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 524/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 8.190e-05, size: 480, ETA: 0:28:15
2025-08-01 08:22:15.505 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 524/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 8.157e-05, size: 320, ETA: 0:28:11
2025-08-01 08:22:19.097 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 524/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.7, lr: 8.124e-05, size: 352, ETA: 0:28:08
2025-08-01 08:22:22.563 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 524/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 8.092e-05, size: 544, ETA: 0:28:04
2025-08-01 08:22:24.047 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:22:30.959 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:22:31.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:22:32.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5059
2025-08-01 08:22:32.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4172
2025-08-01 08:22:32.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2809
2025-08-01 08:22:32.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4013
2025-08-01 08:22:32.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:22:32.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:22:32.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-01 08:22:32.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-01 08:22:32.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.281
2025-08-01 08:22:32.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.401
2025-08-01 08:22:32.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:22:32.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:22:32.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:22:32.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:22:32.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:22:32.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:22:32.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:22:32.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:22:32.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:22:33.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:22:33.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:22:34.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:22:35.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:22:35.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:22:36.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:22:37.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:22:37.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:22:38.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:22:38.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:22:38.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:22:38.580 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:22:38.587 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.88 ms, Average inference time: 8.40 ms

2025-08-01 08:22:38.588 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:22:38.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:22:38.745 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch525
2025-08-01 08:22:41.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 525/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.9, lr: 8.045e-05, size: 352, ETA: 0:27:59
2025-08-01 08:22:45.250 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 525/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 8.012e-05, size: 416, ETA: 0:27:56
2025-08-01 08:22:48.594 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 525/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 7.980e-05, size: 544, ETA: 0:27:52
2025-08-01 08:22:52.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 525/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 7.948e-05, size: 576, ETA: 0:27:49
2025-08-01 08:22:55.908 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 525/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 7.915e-05, size: 576, ETA: 0:27:46
2025-08-01 08:22:59.341 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 525/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 7.883e-05, size: 256, ETA: 0:27:42
2025-08-01 08:23:00.804 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:23:07.794 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:23:09.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:23:09.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4753
2025-08-01 08:23:09.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4039
2025-08-01 08:23:09.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2350
2025-08-01 08:23:09.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3714
2025-08-01 08:23:09.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:23:09.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:23:09.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-01 08:23:09.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:23:09.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:23:10.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:23:11.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:23:12.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:23:13.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:23:14.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:23:14.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:23:15.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:23:16.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:23:17.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:23:17.383 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:23:17.383 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 08:23:17.383 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:23:17.391 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.88 ms, Average inference time: 8.41 ms

2025-08-01 08:23:17.392 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:23:17.489 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:23:17.581 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch526
2025-08-01 08:23:20.902 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 526/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, 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: 7.837e-05, size: 512, ETA: 0:27:37
2025-08-01 08:23:24.239 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 526/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 0.7, lr: 7.805e-05, size: 544, ETA: 0:27:34
2025-08-01 08:23:27.646 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 526/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 1.1, lr: 7.773e-05, size: 256, ETA: 0:27:30
2025-08-01 08:23:31.073 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 526/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 7.741e-05, size: 576, ETA: 0:27:27
2025-08-01 08:23:34.673 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 526/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 7.709e-05, size: 256, ETA: 0:27:23
2025-08-01 08:23:37.999 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 526/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, 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: 7.677e-05, size: 512, ETA: 0:27:20
2025-08-01 08:23:39.590 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:23:46.051 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:23:46.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:23:46.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3933
2025-08-01 08:23:46.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4312
2025-08-01 08:23:46.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1735
2025-08-01 08:23:46.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3327
2025-08-01 08:23:46.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:23:46.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.173
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.333
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:23:46.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:23:46.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:23:46.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:23:47.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:23:47.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:23:47.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:23:47.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:23:48.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:23:48.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:23:48.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:23:49.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:23:49.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:23:49.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:23:49.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 08:23:49.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:23:49.435 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.73 ms, Average inference time: 8.29 ms

2025-08-01 08:23:49.436 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:23:49.519 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:23:49.598 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch527
2025-08-01 08:23:52.801 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 527/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 0.6, lr: 7.631e-05, size: 576, ETA: 0:27:15
2025-08-01 08:23:56.347 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 527/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 7.600e-05, size: 256, ETA: 0:27:11
2025-08-01 08:23:59.568 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 527/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 7.568e-05, size: 416, ETA: 0:27:08
2025-08-01 08:24:02.921 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 527/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 2.4, cls_loss: 0.7, lr: 7.537e-05, size: 576, ETA: 0:27:05
2025-08-01 08:24:06.456 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 527/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, 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: 7.505e-05, size: 544, ETA: 0:27:01
2025-08-01 08:24:09.844 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 527/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 7.474e-05, size: 288, ETA: 0:26:58
2025-08-01 08:24:11.233 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:24:18.067 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:24:18.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:24:19.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4957
2025-08-01 08:24:19.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4278
2025-08-01 08:24:19.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2780
2025-08-01 08:24:19.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4005
2025-08-01 08:24:19.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:24:19.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:24:19.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-01 08:24:19.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-01 08:24:19.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-08-01 08:24:19.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.400
2025-08-01 08:24:19.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:24:19.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:24:19.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:24:19.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:24:19.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:24:19.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:24:19.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:24:19.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:24:19.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:24:20.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:24:20.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:24:21.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:24:22.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:24:22.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:24:23.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:24:24.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:24:24.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:24:25.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:24:25.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:24:25.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:24:25.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:24:25.435 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.87 ms, Average inference time: 8.33 ms

2025-08-01 08:24:25.438 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:24:25.566 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:24:25.648 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch528
2025-08-01 08:24:28.961 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 528/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 9.5, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 4.2, cls_loss: 1.0, lr: 7.429e-05, size: 352, ETA: 0:26:53
2025-08-01 08:24:32.205 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 528/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.6, lr: 7.398e-05, size: 352, ETA: 0:26:49
2025-08-01 08:24:35.512 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 528/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 7.366e-05, size: 448, ETA: 0:26:46
2025-08-01 08:24:38.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 528/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 7.335e-05, size: 576, ETA: 0:26:42
2025-08-01 08:24:42.310 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 528/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 7.304e-05, size: 544, ETA: 0:26:39
2025-08-01 08:24:45.727 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 528/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 7.273e-05, size: 320, ETA: 0:26:35
2025-08-01 08:24:47.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:24:54.094 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:24:54.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:24:55.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4483
2025-08-01 08:24:55.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3491
2025-08-01 08:24:55.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2393
2025-08-01 08:24:55.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3456
2025-08-01 08:24:55.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:24:55.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:24:55.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-08-01 08:24:55.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-08-01 08:24:55.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.239
2025-08-01 08:24:55.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.346
2025-08-01 08:24:55.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:24:55.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:24:55.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:24:55.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:24:55.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:24:55.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:24:55.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:24:55.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:24:55.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:24:55.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:24:56.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:24:57.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:24:57.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:24:58.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:24:58.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:24:59.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:24:59.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:25:00.131 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:25:00.131 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:25:00.131 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 08:25:00.131 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:25:00.140 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.82 ms, Average inference time: 8.31 ms

2025-08-01 08:25:00.142 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:25:00.261 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:25:00.374 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch529
2025-08-01 08:25:03.504 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 529/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.6, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 7.229e-05, size: 352, ETA: 0:26:30
2025-08-01 08:25:06.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 529/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 7.198e-05, size: 256, ETA: 0:26:27
2025-08-01 08:25:10.297 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 529/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, 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: 7.167e-05, size: 512, ETA: 0:26:23
2025-08-01 08:25:13.758 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 529/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 7.136e-05, size: 416, ETA: 0:26:20
2025-08-01 08:25:17.120 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 529/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.7, lr: 7.106e-05, size: 544, ETA: 0:26:17
2025-08-01 08:25:20.341 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 529/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 7.075e-05, size: 288, ETA: 0:26:13
2025-08-01 08:25:21.791 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:25:28.680 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:25:29.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:25:29.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4792
2025-08-01 08:25:29.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4192
2025-08-01 08:25:29.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2645
2025-08-01 08:25:29.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3876
2025-08-01 08:25:29.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:25:29.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:25:29.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-01 08:25:29.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-01 08:25:29.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.388
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:25:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:25:30.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:25:30.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:25:31.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:25:32.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:25:32.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:25:33.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:25:33.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:25:34.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:25:34.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:25:34.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:25:34.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 08:25:34.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:25:34.515 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.86 ms, Average inference time: 8.40 ms

2025-08-01 08:25:34.516 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:25:34.591 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:25:34.673 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch530
2025-08-01 08:25:37.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 530/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 7.031e-05, size: 384, ETA: 0:26:08
2025-08-01 08:25:40.945 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 530/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 7.001e-05, size: 448, ETA: 0:26:05
2025-08-01 08:25:44.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 530/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, 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: 6.970e-05, size: 448, ETA: 0:26:01
2025-08-01 08:25:47.733 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 530/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.004s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 6.940e-05, size: 352, ETA: 0:25:58
2025-08-01 08:25:51.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 530/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.7, cls_loss: 0.6, lr: 6.910e-05, size: 448, ETA: 0:25:54
2025-08-01 08:25:54.395 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 530/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.8, lr: 6.880e-05, size: 320, ETA: 0:25:51
2025-08-01 08:25:55.916 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:26:02.733 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:26:03.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:26:04.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4407
2025-08-01 08:26:04.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3911
2025-08-01 08:26:04.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2281
2025-08-01 08:26:04.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3533
2025-08-01 08:26:04.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:26:04.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:26:04.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-08-01 08:26:04.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-01 08:26:04.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.228
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:26:04.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:26:04.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:26:05.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:26:06.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:26:07.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:26:08.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:26:09.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:26:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:26:11.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:26:12.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:26:13.585 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:26:13.585 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:26:13.585 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 08:26:13.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:26:13.593 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.89 ms, Average inference time: 8.37 ms

2025-08-01 08:26:13.594 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:26:13.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:26:13.757 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch531
2025-08-01 08:26:16.830 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 531/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 6.836e-05, size: 256, ETA: 0:25:46
2025-08-01 08:26:20.355 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 531/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, 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: 6.806e-05, size: 448, ETA: 0:25:42
2025-08-01 08:26:23.617 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 531/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 2.6, cls_loss: 0.6, lr: 6.776e-05, size: 288, ETA: 0:25:39
2025-08-01 08:26:27.071 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 531/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, 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: 6.747e-05, size: 544, ETA: 0:25:35
2025-08-01 08:26:30.606 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 531/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 6.717e-05, size: 512, ETA: 0:25:32
2025-08-01 08:26:34.278 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 531/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 6.687e-05, size: 480, ETA: 0:25:29
2025-08-01 08:26:35.799 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:26:42.751 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:26:43.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:26:43.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4855
2025-08-01 08:26:44.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3892
2025-08-01 08:26:44.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2531
2025-08-01 08:26:44.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3759
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.253
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:26:44.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:26:44.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:26:44.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:26:44.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:26:44.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:26:45.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:26:45.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:26:46.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:26:47.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:26:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:26:48.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:26:48.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:26:49.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:26:49.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:26:49.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:26:49.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:26:49.585 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.87 ms, Average inference time: 8.42 ms

2025-08-01 08:26:49.586 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:26:49.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:26:49.789 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch532
2025-08-01 08:26:53.293 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 532/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.6, lr: 6.644e-05, size: 320, ETA: 0:25:24
2025-08-01 08:26:56.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 532/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, 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: 6.615e-05, size: 352, ETA: 0:25:20
2025-08-01 08:27:00.186 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 532/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 6.585e-05, size: 576, ETA: 0:25:17
2025-08-01 08:27:03.580 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 532/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 6.556e-05, size: 448, ETA: 0:25:13
2025-08-01 08:27:07.122 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 532/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, 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: 6.526e-05, size: 544, ETA: 0:25:10
2025-08-01 08:27:10.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 532/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.5, lr: 6.497e-05, size: 512, ETA: 0:25:06
2025-08-01 08:27:12.048 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:27:18.836 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:27:19.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:27:20.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3621
2025-08-01 08:27:20.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2635
2025-08-01 08:27:20.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1779
2025-08-01 08:27:20.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2678
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.178
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.268
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:27:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:27:20.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:27:20.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:27:20.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:27:20.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:27:20.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:27:20.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:27:21.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:27:22.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:27:23.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:27:23.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:27:24.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:27:25.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:27:26.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:27:26.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:27:27.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:27:27.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 08:27:27.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 08:27:27.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:27:27.769 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.88 ms, Average inference time: 8.33 ms

2025-08-01 08:27:27.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:27:27.844 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:27:27.927 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch533
2025-08-01 08:27:31.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 533/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 6.455e-05, size: 320, ETA: 0:25:01
2025-08-01 08:27:34.612 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 533/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 6.426e-05, size: 288, ETA: 0:24:58
2025-08-01 08:27:37.890 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 533/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.5, lr: 6.396e-05, size: 512, ETA: 0:24:54
2025-08-01 08:27:41.206 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 533/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 6.367e-05, size: 352, ETA: 0:24:51
2025-08-01 08:27:44.442 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 533/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 6.338e-05, size: 512, ETA: 0:24:48
2025-08-01 08:27:47.811 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 533/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 6.310e-05, size: 384, ETA: 0:24:44
2025-08-01 08:27:49.406 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:27:56.140 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:27:56.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:27:57.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4226
2025-08-01 08:27:57.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3870
2025-08-01 08:27:57.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2022
2025-08-01 08:27:57.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3373
2025-08-01 08:27:57.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:27:57.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:27:57.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-08-01 08:27:57.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 08:27:57.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-08-01 08:27:57.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-08-01 08:27:57.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:27:57.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:27:57.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:27:57.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:27:57.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:27:57.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:27:57.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:27:57.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:27:57.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:27:57.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:27:57.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:27:58.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:27:58.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:27:59.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:27:59.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:28:00.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:28:00.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:28:01.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:28:01.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:28:01.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 08:28:01.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:28:01.024 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.79 ms, Average inference time: 8.29 ms

2025-08-01 08:28:01.024 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:28:01.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:28:01.185 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch534
2025-08-01 08:28:04.371 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 534/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.8, lr: 6.268e-05, size: 480, ETA: 0:24:39
2025-08-01 08:28:07.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 534/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.7, lr: 6.239e-05, size: 352, ETA: 0:24:36
2025-08-01 08:28:10.851 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 534/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 6.210e-05, size: 384, ETA: 0:24:32
2025-08-01 08:28:14.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 534/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.5, lr: 6.182e-05, size: 352, ETA: 0:24:29
2025-08-01 08:28:17.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 534/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 6.153e-05, size: 512, ETA: 0:24:25
2025-08-01 08:28:21.259 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 534/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 6.125e-05, size: 352, ETA: 0:24:22
2025-08-01 08:28:22.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:28:29.653 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:28:30.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:28:30.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4905
2025-08-01 08:28:30.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4428
2025-08-01 08:28:30.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2592
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3975
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.443
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.259
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:28:30.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:28:30.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:28:30.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:28:30.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:28:30.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:28:30.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:28:31.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:28:31.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:28:32.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:28:32.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:28:33.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:28:33.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:28:34.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:28:34.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:28:35.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:28:35.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:28:35.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:28:35.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:28:35.298 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.80 ms, Average inference time: 8.33 ms

2025-08-01 08:28:35.300 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:28:35.375 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:28:35.456 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch535
2025-08-01 08:28:38.668 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 535/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 6.084e-05, size: 352, ETA: 0:24:17
2025-08-01 08:28:42.128 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 535/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 6.055e-05, size: 576, ETA: 0:24:13
2025-08-01 08:28:45.783 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 535/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.5, lr: 6.027e-05, size: 576, ETA: 0:24:10
2025-08-01 08:28:49.202 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 535/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, 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: 5.999e-05, size: 416, ETA: 0:24:07
2025-08-01 08:28:52.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 535/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 5.971e-05, size: 544, ETA: 0:24:03
2025-08-01 08:28:56.144 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 535/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.6, lr: 5.943e-05, size: 512, ETA: 0:24:00
2025-08-01 08:28:57.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:29:04.463 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:29:05.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:29:06.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4016
2025-08-01 08:29:06.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3043
2025-08-01 08:29:06.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1922
2025-08-01 08:29:06.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2994
2025-08-01 08:29:06.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:29:06.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:29:06.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 08:29:06.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-01 08:29:06.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.192
2025-08-01 08:29:06.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.299
2025-08-01 08:29:06.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:29:06.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:29:06.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:29:06.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:29:06.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:29:06.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:29:06.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:29:06.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:29:06.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:29:07.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:29:07.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:29:08.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:29:09.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:29:10.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:29:10.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:29:11.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:29:12.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:29:13.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:29:13.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:29:13.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-08-01 08:29:13.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:29:13.229 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.91 ms, Average inference time: 8.46 ms

2025-08-01 08:29:13.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:29:13.307 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:29:13.389 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch536
2025-08-01 08:29:16.617 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 536/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 5.902e-05, size: 448, ETA: 0:23:55
2025-08-01 08:29:19.782 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 536/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.8, lr: 5.874e-05, size: 256, ETA: 0:23:51
2025-08-01 08:29:23.146 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 536/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 5.846e-05, size: 288, ETA: 0:23:48
2025-08-01 08:29:26.687 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 536/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 3.3, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.4, lr: 5.818e-05, size: 576, ETA: 0:23:44
2025-08-01 08:29:30.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 536/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.8, lr: 5.791e-05, size: 288, ETA: 0:23:41
2025-08-01 08:29:33.175 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 536/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.9, lr: 5.763e-05, size: 288, ETA: 0:23:37
2025-08-01 08:29:34.565 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:29:41.453 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:29:42.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:29:42.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5088
2025-08-01 08:29:42.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4357
2025-08-01 08:29:42.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3165
2025-08-01 08:29:42.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4203
2025-08-01 08:29:42.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:29:42.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:29:42.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-01 08:29:42.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-08-01 08:29:42.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-01 08:29:42.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.420
2025-08-01 08:29:42.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:29:42.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:29:42.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:29:42.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:29:42.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:29:42.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:29:42.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:29:42.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:29:42.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:29:43.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:29:44.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:29:44.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:29:45.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:29:46.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:29:46.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:29:47.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:29:48.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:29:48.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:29:48.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 08:29:48.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 08:29:48.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:29:48.925 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.67 ms, Average NMS time: 0.90 ms, Average inference time: 8.56 ms

2025-08-01 08:29:48.926 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:29:49.005 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:29:49.088 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch537
2025-08-01 08:29:52.328 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 537/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 5.723e-05, size: 576, ETA: 0:23:32
2025-08-01 08:29:55.785 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 537/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 5.696e-05, size: 352, ETA: 0:23:29
2025-08-01 08:29:59.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 537/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.9, lr: 5.668e-05, size: 256, ETA: 0:23:25
2025-08-01 08:30:02.552 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 537/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 5.641e-05, size: 448, ETA: 0:23:22
2025-08-01 08:30:05.840 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 537/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 0.8, cls_loss: 0.6, lr: 5.614e-05, size: 416, ETA: 0:23:19
2025-08-01 08:30:09.192 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 537/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.8, lr: 5.586e-05, size: 352, ETA: 0:23:15
2025-08-01 08:30:10.711 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:30:17.578 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:30:18.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:30:18.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4597
2025-08-01 08:30:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3320
2025-08-01 08:30:19.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2230
2025-08-01 08:30:19.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3382
2025-08-01 08:30:19.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.338
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:30:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:30:19.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:30:19.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:30:19.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:30:19.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:30:19.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:30:20.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:30:21.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:30:22.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:30:22.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:30:23.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:30:24.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:30:24.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:30:25.612 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:30:25.612 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:30:25.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 08:30:25.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:30:25.620 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.88 ms, Average inference time: 8.38 ms

2025-08-01 08:30:25.621 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:30:25.704 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:30:25.782 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch538
2025-08-01 08:30:28.870 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 538/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.8, lr: 5.547e-05, size: 320, ETA: 0:23:10
2025-08-01 08:30:32.469 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 538/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.7, lr: 5.520e-05, size: 576, ETA: 0:23:07
2025-08-01 08:30:35.985 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 538/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 5.493e-05, size: 448, ETA: 0:23:03
2025-08-01 08:30:39.327 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 538/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 5.466e-05, size: 576, ETA: 0:23:00
2025-08-01 08:30:42.836 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 538/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.6, lr: 5.439e-05, size: 288, ETA: 0:22:56
2025-08-01 08:30:46.212 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 538/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 5.412e-05, size: 352, ETA: 0:22:53
2025-08-01 08:30:47.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:30:54.368 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:30:55.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:30:55.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4736
2025-08-01 08:30:55.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3959
2025-08-01 08:30:55.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2401
2025-08-01 08:30:55.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3699
2025-08-01 08:30:55.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:30:55.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:30:55.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-01 08:30:55.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-08-01 08:30:55.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-08-01 08:30:55.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-01 08:30:55.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:30:55.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:30:55.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:30:55.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:30:55.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:30:55.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:30:55.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:30:55.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:30:55.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:30:56.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:30:56.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:30:57.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:30:58.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:30:58.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:30:59.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:30:59.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:31:00.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:31:00.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:31:00.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:31:00.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 08:31:00.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:31:00.966 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.87 ms, Average inference time: 8.39 ms

2025-08-01 08:31:00.967 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:31:01.048 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:31:01.139 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch539
2025-08-01 08:31:04.294 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 539/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 5.373e-05, size: 256, ETA: 0:22:48
2025-08-01 08:31:07.443 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 539/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 5.347e-05, size: 416, ETA: 0:22:44
2025-08-01 08:31:10.554 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 539/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 3.3, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 5.320e-05, size: 288, ETA: 0:22:41
2025-08-01 08:31:14.053 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 539/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.8, lr: 5.294e-05, size: 288, ETA: 0:22:38
2025-08-01 08:31:17.177 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 539/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, 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: 5.267e-05, size: 288, ETA: 0:22:34
2025-08-01 08:31:20.638 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 539/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 5.241e-05, size: 480, ETA: 0:22:31
2025-08-01 08:31:22.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:31:28.830 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:31:29.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:31:30.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4415
2025-08-01 08:31:30.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3588
2025-08-01 08:31:30.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1406
2025-08-01 08:31:30.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3136
2025-08-01 08:31:30.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:31:30.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:31:30.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-01 08:31:30.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-01 08:31:30.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.141
2025-08-01 08:31:30.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.314
2025-08-01 08:31:30.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:31:30.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:31:30.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:31:30.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:31:30.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:31:30.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:31:30.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:31:30.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:31:30.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:31:31.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:31:31.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:31:32.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:31:32.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:31:33.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:31:34.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:31:34.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:31:35.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:31:36.224 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:31:36.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:31:36.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 08:31:36.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:31:36.234 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.82 ms, Average inference time: 8.27 ms

2025-08-01 08:31:36.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:31:36.346 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:31:36.460 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch540
2025-08-01 08:31:39.655 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 540/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 5.203e-05, size: 544, ETA: 0:22:26
2025-08-01 08:31:43.206 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 540/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 5.176e-05, size: 384, ETA: 0:22:22
2025-08-01 08:31:46.347 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 540/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 5.150e-05, size: 320, ETA: 0:22:19
2025-08-01 08:31:49.575 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 540/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 1.0, lr: 5.124e-05, size: 544, ETA: 0:22:15
2025-08-01 08:31:52.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 540/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.5, lr: 5.098e-05, size: 576, ETA: 0:22:12
2025-08-01 08:31:56.445 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 540/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 5.072e-05, size: 320, ETA: 0:22:08
2025-08-01 08:31:57.944 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:32:04.756 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:32:05.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:32:05.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3853
2025-08-01 08:32:05.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3059
2025-08-01 08:32:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1596
2025-08-01 08:32:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2836
2025-08-01 08:32:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:32:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:32:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-01 08:32:05.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.160
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.284
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:32:05.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:32:05.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:32:06.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:32:06.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:32:07.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:32:07.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:32:08.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:32:08.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:32:09.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:32:09.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:32:10.178 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:32:10.178 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:32:10.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 08:32:10.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:32:10.192 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.84 ms, Average inference time: 8.22 ms

2025-08-01 08:32:10.193 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:32:10.313 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:32:10.459 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch541
2025-08-01 08:32:13.730 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 541/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 5.034e-05, size: 416, ETA: 0:22:03
2025-08-01 08:32:17.225 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 541/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 5.009e-05, size: 288, ETA: 0:22:00
2025-08-01 08:32:20.723 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 541/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 4.983e-05, size: 416, ETA: 0:21:57
2025-08-01 08:32:24.177 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 541/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 1.5, cls_loss: 0.6, lr: 4.957e-05, size: 512, ETA: 0:21:53
2025-08-01 08:32:27.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 541/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 4.931e-05, size: 544, ETA: 0:21:50
2025-08-01 08:32:30.897 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 541/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, 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: 4.906e-05, size: 352, ETA: 0:21:46
2025-08-01 08:32:32.377 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:32:39.025 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:32:39.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:32:40.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4529
2025-08-01 08:32:40.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3917
2025-08-01 08:32:40.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2486
2025-08-01 08:32:40.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3644
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.364
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:32:40.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:32:40.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:32:40.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:32:40.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:32:40.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:32:40.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:32:40.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:32:40.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:32:41.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:32:41.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:32:42.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:32:42.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:32:43.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:32:43.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:32:44.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:32:44.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:32:44.726 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:32:44.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 08:32:44.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:32:44.733 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.82 ms, Average inference time: 8.26 ms

2025-08-01 08:32:44.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:32:44.842 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:32:44.974 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch542
2025-08-01 08:32:48.082 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 542/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 1.0, lr: 4.869e-05, size: 384, ETA: 0:21:41
2025-08-01 08:32:51.384 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 542/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, 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: 4.843e-05, size: 448, ETA: 0:21:38
2025-08-01 08:32:54.763 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 542/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 3.5, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 4.818e-05, size: 256, ETA: 0:21:34
2025-08-01 08:32:58.296 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 542/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 0.7, cls_loss: 0.6, lr: 4.793e-05, size: 384, ETA: 0:21:31
2025-08-01 08:33:01.760 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 542/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 4.768e-05, size: 384, ETA: 0:21:27
2025-08-01 08:33:05.187 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 542/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.7, lr: 4.742e-05, size: 576, ETA: 0:21:24
2025-08-01 08:33:06.777 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:33:13.585 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:33:14.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:33:14.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4824
2025-08-01 08:33:14.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4292
2025-08-01 08:33:14.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2577
2025-08-01 08:33:14.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3898
2025-08-01 08:33:14.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.390
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:33:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:33:14.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:33:15.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:33:15.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:33:16.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:33:16.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:33:17.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:33:17.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:33:18.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:33:19.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:33:19.519 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:33:19.519 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:33:19.519 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 08:33:19.520 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:33:19.526 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.86 ms, Average inference time: 8.34 ms

2025-08-01 08:33:19.528 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:33:19.604 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:33:19.683 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch543
2025-08-01 08:33:23.083 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 543/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 4.706e-05, size: 512, ETA: 0:21:19
2025-08-01 08:33:26.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 543/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 1.0, lr: 4.681e-05, size: 288, ETA: 0:21:16
2025-08-01 08:33:29.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 543/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 4.656e-05, size: 256, ETA: 0:21:12
2025-08-01 08:33:32.959 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 543/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 4.631e-05, size: 416, ETA: 0:21:09
2025-08-01 08:33:36.282 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 543/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 4.607e-05, size: 480, ETA: 0:21:05
2025-08-01 08:33:39.430 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 543/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 4.582e-05, size: 288, ETA: 0:21:02
2025-08-01 08:33:40.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:33:47.810 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:33:48.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:33:49.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4945
2025-08-01 08:33:49.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4019
2025-08-01 08:33:49.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2903
2025-08-01 08:33:49.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3956
2025-08-01 08:33:49.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:33:49.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:33:49.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-01 08:33:49.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 08:33:49.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-01 08:33:49.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-08-01 08:33:49.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:33:49.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:33:49.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:33:49.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:33:49.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:33:49.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:33:49.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:33:49.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:33:49.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:33:50.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:33:51.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:33:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:33:53.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:33:54.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:33:55.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:33:56.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:33:56.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:33:57.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:33:57.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:33:57.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:33:57.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:33:57.897 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.87 ms, Average inference time: 8.35 ms

2025-08-01 08:33:57.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:33:57.978 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:33:58.060 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch544
2025-08-01 08:34:01.438 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 544/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 4.546e-05, size: 512, ETA: 0:20:57
2025-08-01 08:34:04.823 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 544/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 4.521e-05, size: 480, ETA: 0:20:53
2025-08-01 08:34:08.157 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 544/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, 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: 4.497e-05, size: 384, ETA: 0:20:50
2025-08-01 08:34:11.538 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 544/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.5, lr: 4.472e-05, size: 544, ETA: 0:20:46
2025-08-01 08:34:14.858 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 544/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.9, lr: 4.448e-05, size: 352, ETA: 0:20:43
2025-08-01 08:34:18.352 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 544/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.7, lr: 4.424e-05, size: 384, ETA: 0:20:40
2025-08-01 08:34:19.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:34:26.517 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:34:27.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:34:27.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4901
2025-08-01 08:34:27.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4239
2025-08-01 08:34:27.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2358
2025-08-01 08:34:27.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3833
2025-08-01 08:34:27.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:34:27.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:34:27.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-01 08:34:27.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 08:34:27.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-08-01 08:34:27.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.383
2025-08-01 08:34:27.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:34:27.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:34:27.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:34:27.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:34:27.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:34:27.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:34:27.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:34:27.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:34:27.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:34:28.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:34:28.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:34:29.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:34:30.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:34:30.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:34:31.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:34:31.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:34:32.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:34:32.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:34:32.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:34:32.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:34:32.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:34:32.768 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.82 ms, Average inference time: 8.39 ms

2025-08-01 08:34:32.769 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:34:32.846 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:34:32.984 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch545
2025-08-01 08:34:36.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 545/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 4.389e-05, size: 256, ETA: 0:20:35
2025-08-01 08:34:39.424 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 545/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 4.364e-05, size: 320, ETA: 0:20:31
2025-08-01 08:34:42.795 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 545/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 4.340e-05, size: 512, ETA: 0:20:28
2025-08-01 08:34:46.337 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 545/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 4.316e-05, size: 512, ETA: 0:20:24
2025-08-01 08:34:49.666 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 545/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 4.292e-05, size: 512, ETA: 0:20:21
2025-08-01 08:34:53.012 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 545/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 4.269e-05, size: 384, ETA: 0:20:17
2025-08-01 08:34:54.505 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:35:01.378 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:35:02.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:35:02.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4000
2025-08-01 08:35:02.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3068
2025-08-01 08:35:02.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1704
2025-08-01 08:35:02.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2924
2025-08-01 08:35:02.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:35:02.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:35:02.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-01 08:35:02.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-01 08:35:02.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.170
2025-08-01 08:35:02.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.292
2025-08-01 08:35:02.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:35:02.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:35:02.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:35:02.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:35:02.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:35:02.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:35:02.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:35:02.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:35:02.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:35:03.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:35:03.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:35:04.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:35:05.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:35:05.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:35:06.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:35:06.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:35:07.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:35:07.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:35:07.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:35:07.964 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 08:35:07.964 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:35:07.973 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.85 ms, Average inference time: 8.40 ms

2025-08-01 08:35:07.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:35:08.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:35:08.202 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch546
2025-08-01 08:35:11.403 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 546/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 4.234e-05, size: 512, ETA: 0:20:12
2025-08-01 08:35:14.657 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 546/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 9.4, iou_loss: 3.4, l1_loss: 1.8, conf_loss: 3.5, cls_loss: 0.8, lr: 4.210e-05, size: 288, ETA: 0:20:09
2025-08-01 08:35:17.918 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 546/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 4.187e-05, size: 288, ETA: 0:20:05
2025-08-01 08:35:21.317 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 546/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.8, lr: 4.163e-05, size: 576, ETA: 0:20:02
2025-08-01 08:35:24.855 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 546/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, 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: 4.139e-05, size: 416, ETA: 0:19:59
2025-08-01 08:35:28.075 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 546/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, 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: 4.116e-05, size: 480, ETA: 0:19:55
2025-08-01 08:35:29.552 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:35:36.317 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:35:36.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:35:37.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4838
2025-08-01 08:35:37.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4385
2025-08-01 08:35:37.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2409
2025-08-01 08:35:37.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3877
2025-08-01 08:35:37.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:35:37.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:35:37.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-01 08:35:37.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 08:35:37.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.241
2025-08-01 08:35:37.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.388
2025-08-01 08:35:37.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:35:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:35:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:35:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:35:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:35:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:35:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:35:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:35:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:35:37.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:35:38.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:35:39.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:35:39.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:35:40.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:35:40.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:35:41.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:35:41.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:35:42.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:35:42.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:35:42.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 08:35:42.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:35:42.069 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.83 ms, Average inference time: 8.38 ms

2025-08-01 08:35:42.070 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:35:42.197 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:35:42.276 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch547
2025-08-01 08:35:45.889 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 547/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 10.1, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 4.7, cls_loss: 1.0, lr: 4.082e-05, size: 384, ETA: 0:19:50
2025-08-01 08:35:49.255 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 547/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.7, lr: 4.059e-05, size: 352, ETA: 0:19:47
2025-08-01 08:35:52.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 547/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 4.035e-05, size: 544, ETA: 0:19:43
2025-08-01 08:35:56.112 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 547/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 1.0, cls_loss: 0.5, lr: 4.012e-05, size: 384, ETA: 0:19:40
2025-08-01 08:35:59.397 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 547/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 3.989e-05, size: 448, ETA: 0:19:36
2025-08-01 08:36:02.857 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 547/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 3.966e-05, size: 288, ETA: 0:19:33
2025-08-01 08:36:04.241 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:36:11.076 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:36:11.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:36:11.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4444
2025-08-01 08:36:12.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3866
2025-08-01 08:36:12.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2308
2025-08-01 08:36:12.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3539
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.231
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.354
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:36:12.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:36:12.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:36:12.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:36:12.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:36:12.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:36:12.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:36:13.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:36:13.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:36:14.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:36:14.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:36:14.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:36:15.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:36:15.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:36:16.132 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:36:16.132 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:36:16.132 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 08:36:16.132 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:36:16.139 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.63 ms, Average NMS time: 0.84 ms, Average inference time: 8.47 ms

2025-08-01 08:36:16.140 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:36:16.224 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:36:16.303 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch548
2025-08-01 08:36:19.597 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 548/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 3.933e-05, size: 384, ETA: 0:19:28
2025-08-01 08:36:23.121 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 548/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 3.910e-05, size: 576, ETA: 0:19:24
2025-08-01 08:36:26.584 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 548/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 3.887e-05, size: 512, ETA: 0:19:21
2025-08-01 08:36:30.117 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 548/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 3.864e-05, size: 576, ETA: 0:19:18
2025-08-01 08:36:33.540 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 548/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 3.6, cls_loss: 0.7, lr: 3.842e-05, size: 256, ETA: 0:19:14
2025-08-01 08:36:37.021 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 548/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 3.819e-05, size: 448, ETA: 0:19:11
2025-08-01 08:36:38.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:36:45.223 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:36:45.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:36:45.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4753
2025-08-01 08:36:46.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4158
2025-08-01 08:36:46.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2599
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3837
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.260
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.384
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:36:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:36:46.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:36:46.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:36:46.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:36:46.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:36:46.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:36:46.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:36:46.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:36:46.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:36:46.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:36:47.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:36:47.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:36:48.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:36:48.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:36:48.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:36:49.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:36:49.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:36:49.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:36:49.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:36:49.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:36:49.767 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.82 ms, Average inference time: 8.36 ms

2025-08-01 08:36:49.769 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:36:49.852 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:36:49.933 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch549
2025-08-01 08:36:52.948 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 549/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 3.786e-05, size: 256, ETA: 0:19:06
2025-08-01 08:36:56.138 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 549/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 3.764e-05, size: 544, ETA: 0:19:02
2025-08-01 08:36:59.545 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 549/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 3.7, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.6, lr: 3.741e-05, size: 352, ETA: 0:18:59
2025-08-01 08:37:02.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 549/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 4.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.6, lr: 3.719e-05, size: 448, ETA: 0:18:55
2025-08-01 08:37:06.105 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 549/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.9, lr: 3.697e-05, size: 256, ETA: 0:18:52
2025-08-01 08:37:09.289 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 549/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.6, cls_loss: 0.7, lr: 3.675e-05, size: 320, ETA: 0:18:48
2025-08-01 08:37:10.697 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:37:17.598 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:37:18.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:37:18.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4552
2025-08-01 08:37:18.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4212
2025-08-01 08:37:18.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2831
2025-08-01 08:37:18.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3865
2025-08-01 08:37:18.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:37:18.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:37:18.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-01 08:37:18.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.283
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:37:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:37:18.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:37:19.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:37:19.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:37:20.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:37:20.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:37:21.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:37:22.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:37:22.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:37:23.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:37:23.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:37:23.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:37:23.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 08:37:23.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:37:23.653 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.87 ms, Average inference time: 8.40 ms

2025-08-01 08:37:23.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:37:23.733 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:37:23.812 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch550
2025-08-01 08:37:27.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 550/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.7, lr: 3.643e-05, size: 576, ETA: 0:18:43
2025-08-01 08:37:30.895 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 550/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.188s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.8, lr: 3.621e-05, size: 320, ETA: 0:18:40
2025-08-01 08:37:34.420 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 550/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.5, lr: 3.599e-05, size: 480, ETA: 0:18:37
2025-08-01 08:37:37.948 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 550/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, 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: 3.577e-05, size: 256, ETA: 0:18:33
2025-08-01 08:37:41.436 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 550/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 3.555e-05, size: 512, ETA: 0:18:30
2025-08-01 08:37:44.795 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 550/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 3.533e-05, size: 448, ETA: 0:18:26
2025-08-01 08:37:46.439 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:37:53.279 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:37:53.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:37:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4714
2025-08-01 08:37:54.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4133
2025-08-01 08:37:54.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3016
2025-08-01 08:37:54.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3954
2025-08-01 08:37:54.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:37:54.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:37:54.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-01 08:37:54.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:37:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:37:54.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:37:54.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:37:55.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:37:55.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:37:56.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:37:56.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:37:57.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:37:57.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:37:58.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:37:58.820 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:37:58.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:37:58.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:37:58.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:37:58.830 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.84 ms, Average inference time: 8.32 ms

2025-08-01 08:37:58.831 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:37:58.928 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:37:59.047 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch551
2025-08-01 08:38:02.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 551/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.149s, 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: 3.502e-05, size: 288, ETA: 0:18:21
2025-08-01 08:38:05.399 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 551/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 3.480e-05, size: 352, ETA: 0:18:18
2025-08-01 08:38:08.727 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 551/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 3.458e-05, size: 448, ETA: 0:18:14
2025-08-01 08:38:12.069 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 551/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 3.437e-05, size: 384, ETA: 0:18:11
2025-08-01 08:38:15.422 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 551/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 3.415e-05, size: 256, ETA: 0:18:08
2025-08-01 08:38:18.910 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 551/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 3.394e-05, size: 256, ETA: 0:18:04
2025-08-01 08:38:20.406 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:38:27.195 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:38:28.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:38:28.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4740
2025-08-01 08:38:29.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3850
2025-08-01 08:38:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2418
2025-08-01 08:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3669
2025-08-01 08:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-01 08:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-01 08:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.367
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:38:29.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:38:29.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:38:30.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:38:31.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:38:32.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:38:32.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:38:33.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:38:34.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:38:35.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:38:35.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:38:35.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:38:35.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 08:38:35.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:38:35.870 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.88 ms, Average inference time: 8.31 ms

2025-08-01 08:38:35.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:38:35.981 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:38:36.080 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch552
2025-08-01 08:38:39.265 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 552/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 3.363e-05, size: 256, ETA: 0:17:59
2025-08-01 08:38:42.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 552/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 1.1, lr: 3.342e-05, size: 288, ETA: 0:17:56
2025-08-01 08:38:46.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 552/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.176s, data_time: 0.005s, total_loss: 5.2, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.7, lr: 3.321e-05, size: 576, ETA: 0:17:52
2025-08-01 08:38:49.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 552/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.7, lr: 3.300e-05, size: 320, ETA: 0:17:49
2025-08-01 08:38:52.741 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 552/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.8, lr: 3.279e-05, size: 416, ETA: 0:17:45
2025-08-01 08:38:56.257 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 552/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, 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: 3.258e-05, size: 576, ETA: 0:17:42
2025-08-01 08:38:57.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:39:04.530 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:39:05.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:39:05.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4571
2025-08-01 08:39:05.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3789
2025-08-01 08:39:05.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2212
2025-08-01 08:39:05.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3524
2025-08-01 08:39:05.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:39:05.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:39:05.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-01 08:39:05.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-01 08:39:05.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-08-01 08:39:05.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-08-01 08:39:05.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:39:05.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:39:05.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:39:05.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:39:05.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:39:05.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:39:05.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:39:05.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:39:05.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:39:06.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:39:06.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:39:07.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:39:07.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:39:08.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:39:08.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:39:09.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:39:09.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:39:10.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:39:10.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 08:39:10.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 08:39:10.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:39:10.474 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.84 ms, Average inference time: 8.40 ms

2025-08-01 08:39:10.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:39:10.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:39:10.637 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch553
2025-08-01 08:39:13.812 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 553/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.7, lr: 3.228e-05, size: 288, ETA: 0:17:37
2025-08-01 08:39:17.163 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 553/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 3.207e-05, size: 416, ETA: 0:17:34
2025-08-01 08:39:20.554 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 553/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 3.186e-05, size: 544, ETA: 0:17:30
2025-08-01 08:39:23.862 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 553/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 3.166e-05, size: 288, ETA: 0:17:27
2025-08-01 08:39:27.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 553/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.7, lr: 3.145e-05, size: 576, ETA: 0:17:23
2025-08-01 08:39:31.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 553/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.5, lr: 3.124e-05, size: 544, ETA: 0:17:20
2025-08-01 08:39:32.588 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:39:39.506 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:39:40.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:39:40.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3960
2025-08-01 08:39:40.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2991
2025-08-01 08:39:40.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1631
2025-08-01 08:39:40.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2861
2025-08-01 08:39:40.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:39:40.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:39:40.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-08-01 08:39:40.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-08-01 08:39:40.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.163
2025-08-01 08:39:40.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.286
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:39:40.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:39:41.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:39:42.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:39:42.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:39:43.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:39:44.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:39:44.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:39:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:39:45.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:39:46.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:39:46.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:39:46.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-08-01 08:39:46.639 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:39:46.646 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.87 ms, Average inference time: 8.47 ms

2025-08-01 08:39:46.647 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:39:46.724 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:39:46.806 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch554
2025-08-01 08:39:49.995 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 554/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 3.095e-05, size: 576, ETA: 0:17:15
2025-08-01 08:39:53.402 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 554/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 3.074e-05, size: 320, ETA: 0:17:11
2025-08-01 08:39:56.857 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 554/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, 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: 3.054e-05, size: 576, ETA: 0:17:08
2025-08-01 08:40:00.326 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 554/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 3.034e-05, size: 384, ETA: 0:17:05
2025-08-01 08:40:03.503 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 554/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 3.014e-05, size: 288, ETA: 0:17:01
2025-08-01 08:40:06.746 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 554/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.8, lr: 2.994e-05, size: 544, ETA: 0:16:58
2025-08-01 08:40:08.277 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:40:15.171 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:40:15.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:40:16.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4756
2025-08-01 08:40:16.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3939
2025-08-01 08:40:16.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2640
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3778
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.378
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:40:16.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:40:16.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:40:16.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:40:16.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:40:16.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:40:16.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:40:16.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:40:17.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:40:17.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:40:18.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:40:18.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:40:19.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:40:19.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:40:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:40:20.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:40:21.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:40:21.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:40:21.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:40:21.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:40:21.544 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.84 ms, Average inference time: 8.31 ms

2025-08-01 08:40:21.545 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:40:21.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:40:21.704 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch555
2025-08-01 08:40:24.854 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 555/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.8, lr: 2.965e-05, size: 384, ETA: 0:16:53
2025-08-01 08:40:28.400 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 555/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 2.945e-05, size: 576, ETA: 0:16:49
2025-08-01 08:40:32.074 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 555/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.5, lr: 2.925e-05, size: 512, ETA: 0:16:46
2025-08-01 08:40:35.597 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 555/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 3.6, iou_loss: 0.9, l1_loss: 0.4, conf_loss: 2.0, cls_loss: 0.3, lr: 2.905e-05, size: 352, ETA: 0:16:42
2025-08-01 08:40:39.031 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 555/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 2.885e-05, size: 384, ETA: 0:16:39
2025-08-01 08:40:42.538 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 555/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 2.866e-05, size: 288, ETA: 0:16:36
2025-08-01 08:40:44.076 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:40:50.894 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:40:51.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:40:51.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4564
2025-08-01 08:40:52.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4365
2025-08-01 08:40:52.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2481
2025-08-01 08:40:52.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3803
2025-08-01 08:40:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:40:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:40:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-01 08:40:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 08:40:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.248
2025-08-01 08:40:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-08-01 08:40:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:40:52.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:40:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:40:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:40:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:40:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:40:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:40:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:40:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:40:52.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:40:53.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:40:53.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:40:54.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:40:54.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:40:55.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:40:55.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:40:56.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:40:56.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:40:56.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:40:56.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:40:56.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:40:56.673 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.65 ms, Average NMS time: 0.83 ms, Average inference time: 8.47 ms

2025-08-01 08:40:56.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:40:56.758 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:40:56.837 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch556
2025-08-01 08:40:59.993 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 556/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 2.837e-05, size: 544, ETA: 0:16:31
2025-08-01 08:41:03.371 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 556/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 2.818e-05, size: 416, ETA: 0:16:27
2025-08-01 08:41:06.928 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 556/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 2.799e-05, size: 448, ETA: 0:16:24
2025-08-01 08:41:10.327 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 556/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.8, lr: 2.779e-05, size: 544, ETA: 0:16:20
2025-08-01 08:41:13.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 556/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 2.760e-05, size: 256, ETA: 0:16:17
2025-08-01 08:41:17.182 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 556/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 1.1, lr: 2.741e-05, size: 256, ETA: 0:16:13
2025-08-01 08:41:18.667 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:41:25.353 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:41:25.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:41:26.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4128
2025-08-01 08:41:26.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3476
2025-08-01 08:41:26.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2099
2025-08-01 08:41:26.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3234
2025-08-01 08:41:26.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:41:26.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:41:26.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 08:41:26.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-08-01 08:41:26.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.210
2025-08-01 08:41:26.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.323
2025-08-01 08:41:26.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:41:26.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:41:26.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:41:26.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:41:26.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:41:26.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:41:26.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:41:26.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:41:26.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:41:26.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:41:27.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:41:27.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:41:28.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:41:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:41:29.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:41:29.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:41:30.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:41:30.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:41:30.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 08:41:30.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 08:41:30.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:41:30.932 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.86 ms, Average inference time: 8.35 ms

2025-08-01 08:41:30.933 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:41:31.047 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:41:31.159 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch557
2025-08-01 08:41:34.412 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 557/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 2.713e-05, size: 288, ETA: 0:16:08
2025-08-01 08:41:38.026 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 557/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 4.0, cls_loss: 1.1, lr: 2.694e-05, size: 288, ETA: 0:16:05
2025-08-01 08:41:41.396 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 557/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 2.675e-05, size: 576, ETA: 0:16:02
2025-08-01 08:41:45.126 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 557/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 2.656e-05, size: 512, ETA: 0:15:58
2025-08-01 08:41:48.500 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 557/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, 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: 2.637e-05, size: 512, ETA: 0:15:55
2025-08-01 08:41:51.902 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 557/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 2.618e-05, size: 512, ETA: 0:15:51
2025-08-01 08:41:53.367 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:42:00.206 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:42:00.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:42:01.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5053
2025-08-01 08:42:01.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4256
2025-08-01 08:42:01.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3408
2025-08-01 08:42:01.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4239
2025-08-01 08:42:01.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:42:01.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:42:01.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-01 08:42:01.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.424
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:42:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:42:01.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:42:02.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:42:02.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:42:03.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:42:03.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:42:04.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:42:05.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:42:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:42:06.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:42:06.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:42:06.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:42:06.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 08:42:06.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:42:06.860 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.85 ms, Average inference time: 8.36 ms

2025-08-01 08:42:06.861 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:42:06.946 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:42:07.047 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch558
2025-08-01 08:42:10.372 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 558/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 9.1, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 3.6, cls_loss: 0.8, lr: 2.591e-05, size: 256, ETA: 0:15:46
2025-08-01 08:42:13.877 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 558/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 1.0, lr: 2.572e-05, size: 352, ETA: 0:15:43
2025-08-01 08:42:17.352 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 558/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.005s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 2.554e-05, size: 448, ETA: 0:15:39
2025-08-01 08:42:20.843 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 558/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 2.535e-05, size: 544, ETA: 0:15:36
2025-08-01 08:42:24.223 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 558/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.5, lr: 2.517e-05, size: 320, ETA: 0:15:33
2025-08-01 08:42:27.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 558/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 2.499e-05, size: 512, ETA: 0:15:29
2025-08-01 08:42:29.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:42:35.786 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:42:36.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:42:37.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4929
2025-08-01 08:42:37.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4054
2025-08-01 08:42:37.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2854
2025-08-01 08:42:37.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3946
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:42:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:42:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:42:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:42:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:42:37.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:42:38.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:42:39.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:42:40.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:42:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:42:41.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:42:42.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:42:42.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:42:43.387 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:42:43.387 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:42:43.387 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 08:42:43.387 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:42:43.394 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.85 ms, Average inference time: 8.33 ms

2025-08-01 08:42:43.396 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:42:43.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:42:43.631 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch559
2025-08-01 08:42:46.898 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 559/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, 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: 2.472e-05, size: 480, ETA: 0:15:24
2025-08-01 08:42:50.360 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 559/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 2.454e-05, size: 512, ETA: 0:15:21
2025-08-01 08:42:53.684 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 559/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 2.436e-05, size: 288, ETA: 0:15:17
2025-08-01 08:42:56.908 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 559/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 1.3, lr: 2.418e-05, size: 288, ETA: 0:15:14
2025-08-01 08:43:00.284 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 559/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 2.400e-05, size: 384, ETA: 0:15:10
2025-08-01 08:43:03.891 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 559/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.7, lr: 2.382e-05, size: 320, ETA: 0:15:07
2025-08-01 08:43:05.334 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:43:12.038 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:43:12.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:43:13.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4447
2025-08-01 08:43:13.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4133
2025-08-01 08:43:13.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2540
2025-08-01 08:43:13.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3707
2025-08-01 08:43:13.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:43:13.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:43:13.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:43:13.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:43:13.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:43:14.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:43:14.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:43:15.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:43:15.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:43:16.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:43:16.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:43:17.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:43:17.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:43:17.706 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:43:17.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 08:43:17.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:43:17.716 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.84 ms, Average inference time: 8.21 ms

2025-08-01 08:43:17.716 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:43:17.834 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:43:17.931 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch560
2025-08-01 08:43:21.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 560/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.7, lr: 2.356e-05, size: 576, ETA: 0:15:02
2025-08-01 08:43:24.677 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 560/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.165s, 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: 2.338e-05, size: 480, ETA: 0:14:59
2025-08-01 08:43:28.259 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 560/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.173s, 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: 2.320e-05, size: 544, ETA: 0:14:55
2025-08-01 08:43:31.669 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 560/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.8, lr: 2.303e-05, size: 288, ETA: 0:14:52
2025-08-01 08:43:34.900 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 560/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.0, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.6, lr: 2.285e-05, size: 544, ETA: 0:14:48
2025-08-01 08:43:38.262 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 560/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 2.268e-05, size: 448, ETA: 0:14:45
2025-08-01 08:43:39.897 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:43:46.656 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:43:47.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:43:47.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4935
2025-08-01 08:43:47.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4179
2025-08-01 08:43:47.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2674
2025-08-01 08:43:47.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3929
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.393
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:43:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:43:47.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:43:47.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:43:47.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:43:47.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:43:47.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:43:48.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:43:48.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:43:49.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:43:49.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:43:50.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:43:51.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:43:51.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:43:52.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:43:52.633 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:43:52.633 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:43:52.633 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 08:43:52.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:43:52.649 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.82 ms, Average inference time: 8.35 ms

2025-08-01 08:43:52.650 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:43:52.768 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:43:52.888 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch561
2025-08-01 08:43:56.073 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 561/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.157s, 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: 2.242e-05, size: 480, ETA: 0:14:40
2025-08-01 08:43:59.467 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 561/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 2.225e-05, size: 384, ETA: 0:14:36
2025-08-01 08:44:02.816 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 561/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.004s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 2.208e-05, size: 480, ETA: 0:14:33
2025-08-01 08:44:06.059 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 561/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 2.190e-05, size: 288, ETA: 0:14:29
2025-08-01 08:44:09.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 561/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 2.173e-05, size: 384, ETA: 0:14:26
2025-08-01 08:44:12.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 561/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 2.156e-05, size: 384, ETA: 0:14:23
2025-08-01 08:44:13.949 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:44:20.693 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:44:21.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:44:22.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5133
2025-08-01 08:44:22.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4605
2025-08-01 08:44:22.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2863
2025-08-01 08:44:22.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4200
2025-08-01 08:44:22.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:44:22.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:44:22.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-01 08:44:22.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-08-01 08:44:22.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-08-01 08:44:22.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.420
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:44:22.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:44:23.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:44:23.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:44:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:44:25.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:44:26.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:44:26.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:44:27.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:44:28.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:44:28.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:44:28.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 08:44:28.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-01 08:44:28.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:44:28.995 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.88 ms, Average inference time: 8.43 ms

2025-08-01 08:44:28.996 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:44:29.086 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:44:29.177 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch562
2025-08-01 08:44:32.438 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 562/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 2.132e-05, size: 384, ETA: 0:14:18
2025-08-01 08:44:35.866 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 562/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 2.115e-05, size: 352, ETA: 0:14:14
2025-08-01 08:44:39.353 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 562/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 2.098e-05, size: 352, ETA: 0:14:11
2025-08-01 08:44:42.662 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 562/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 2.081e-05, size: 352, ETA: 0:14:07
2025-08-01 08:44:45.921 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 562/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.4, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.4, lr: 2.064e-05, size: 416, ETA: 0:14:04
2025-08-01 08:44:49.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 562/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.6, lr: 2.048e-05, size: 256, ETA: 0:14:00
2025-08-01 08:44:50.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:44:57.258 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:44:58.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:44:59.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4721
2025-08-01 08:44:59.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3903
2025-08-01 08:44:59.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2663
2025-08-01 08:44:59.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3762
2025-08-01 08:44:59.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:44:59.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:44:59.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:44:59.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:44:59.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:44:59.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:44:59.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:45:01.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:45:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:45:03.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:45:04.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:45:05.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:45:07.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:45:08.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:45:09.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:45:11.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:45:11.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:45:11.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:45:11.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:45:11.083 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.90 ms, Average inference time: 8.27 ms

2025-08-01 08:45:11.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:45:11.224 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:45:11.351 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch563
2025-08-01 08:45:14.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 563/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 3.2, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.024e-05, size: 256, ETA: 0:13:55
2025-08-01 08:45:18.008 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 563/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.8, lr: 2.007e-05, size: 544, ETA: 0:13:52
2025-08-01 08:45:21.574 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 563/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 9.1, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 3.7, cls_loss: 0.9, lr: 1.991e-05, size: 576, ETA: 0:13:49
2025-08-01 08:45:25.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 563/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 1.974e-05, size: 576, ETA: 0:13:45
2025-08-01 08:45:28.746 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 563/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.7, lr: 1.958e-05, size: 416, ETA: 0:13:42
2025-08-01 08:45:32.140 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 563/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, 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: 1.942e-05, size: 480, ETA: 0:13:38
2025-08-01 08:45:33.647 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:45:40.460 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:45:41.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:45:42.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5084
2025-08-01 08:45:42.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4194
2025-08-01 08:45:42.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2952
2025-08-01 08:45:42.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4077
2025-08-01 08:45:42.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:45:42.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:45:42.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-08-01 08:45:42.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-01 08:45:42.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-01 08:45:42.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.408
2025-08-01 08:45:42.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:45:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:45:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:45:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:45:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:45:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:45:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:45:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:45:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:45:43.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:45:44.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:45:45.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:45:46.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:45:47.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:45:47.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:45:48.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:45:49.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:45:50.622 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:45:50.622 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:45:50.622 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 08:45:50.623 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:45:50.630 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.88 ms, Average inference time: 8.43 ms

2025-08-01 08:45:50.632 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:45:50.716 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:45:50.796 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch564
2025-08-01 08:45:53.986 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 564/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.918e-05, size: 288, ETA: 0:13:33
2025-08-01 08:45:57.224 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 564/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 1.902e-05, size: 448, ETA: 0:13:30
2025-08-01 08:46:00.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 564/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.886e-05, size: 480, ETA: 0:13:26
2025-08-01 08:46:03.966 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 564/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.5, lr: 1.870e-05, size: 480, ETA: 0:13:23
2025-08-01 08:46:07.194 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 564/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.159s, 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: 1.855e-05, size: 352, ETA: 0:13:20
2025-08-01 08:46:10.414 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 564/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.839e-05, size: 320, ETA: 0:13:16
2025-08-01 08:46:11.843 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:46:18.628 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:46:19.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:46:20.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4912
2025-08-01 08:46:20.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3870
2025-08-01 08:46:20.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3045
2025-08-01 08:46:20.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3942
2025-08-01 08:46:20.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:46:20.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:46:20.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.394
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:46:20.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:46:20.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:46:20.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:46:20.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:46:21.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:46:22.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:46:23.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:46:24.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:46:24.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:46:25.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:46:26.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:46:27.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:46:28.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:46:28.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:46:28.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-01 08:46:28.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:46:28.347 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.90 ms, Average inference time: 8.32 ms

2025-08-01 08:46:28.348 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:46:28.431 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:46:28.512 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch565
2025-08-01 08:46:31.573 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 565/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.816e-05, size: 256, ETA: 0:13:11
2025-08-01 08:46:35.022 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 565/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 1.800e-05, size: 352, ETA: 0:13:08
2025-08-01 08:46:38.446 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 565/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 1.785e-05, size: 576, ETA: 0:13:04
2025-08-01 08:46:41.863 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 565/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 1.769e-05, size: 544, ETA: 0:13:01
2025-08-01 08:46:45.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 565/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.754e-05, size: 256, ETA: 0:12:57
2025-08-01 08:46:48.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 565/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.8, lr: 1.738e-05, size: 320, ETA: 0:12:54
2025-08-01 08:46:49.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:46:56.815 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:46:57.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:46:58.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5030
2025-08-01 08:46:58.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4318
2025-08-01 08:46:58.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2584
2025-08-01 08:46:58.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3977
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:46:58.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:46:58.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:46:58.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:46:58.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:46:58.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:46:59.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:47:00.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:47:01.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:47:02.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:47:02.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:47:03.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:47:04.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:47:05.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:47:06.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:47:06.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:47:06.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-01 08:47:06.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:47:06.380 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.89 ms, Average inference time: 8.38 ms

2025-08-01 08:47:06.381 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:47:06.457 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:47:06.549 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch566
2025-08-01 08:47:09.681 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 566/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.716e-05, size: 448, ETA: 0:12:49
2025-08-01 08:47:12.981 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 566/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 1.701e-05, size: 480, ETA: 0:12:46
2025-08-01 08:47:16.248 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 566/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.8, lr: 1.686e-05, size: 288, ETA: 0:12:42
2025-08-01 08:47:19.873 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 566/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 1.671e-05, size: 320, ETA: 0:12:39
2025-08-01 08:47:23.233 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 566/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.656e-05, size: 384, ETA: 0:12:35
2025-08-01 08:47:26.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 566/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.175s, 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: 1.641e-05, size: 480, ETA: 0:12:32
2025-08-01 08:47:28.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:47:34.957 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:47:35.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:47:35.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3330
2025-08-01 08:47:36.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2135
2025-08-01 08:47:36.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1530
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2332
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.233
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:47:36.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:47:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:47:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:47:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:47:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:47:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:47:36.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:47:36.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:47:37.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:47:37.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:47:38.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:47:38.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:47:39.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:47:39.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:47:40.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:47:40.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:47:40.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 08:47:40.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-08-01 08:47:40.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:47:40.469 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.82 ms, Average inference time: 8.38 ms

2025-08-01 08:47:40.470 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:47:40.597 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:47:40.681 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch567
2025-08-01 08:47:43.916 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 567/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 1.619e-05, size: 320, ETA: 0:12:27
2025-08-01 08:47:47.374 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 567/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.8, lr: 1.605e-05, size: 448, ETA: 0:12:23
2025-08-01 08:47:50.848 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 567/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.590e-05, size: 384, ETA: 0:12:20
2025-08-01 08:47:54.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 567/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.5, lr: 1.575e-05, size: 352, ETA: 0:12:17
2025-08-01 08:47:57.443 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 567/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 1.561e-05, size: 448, ETA: 0:12:13
2025-08-01 08:48:00.797 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 567/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 1.546e-05, size: 480, ETA: 0:12:10
2025-08-01 08:48:02.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:48:09.120 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:48:09.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:48:10.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4535
2025-08-01 08:48:10.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4008
2025-08-01 08:48:10.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2020
2025-08-01 08:48:10.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3521
2025-08-01 08:48:10.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:48:10.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:48:10.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-01 08:48:10.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-08-01 08:48:10.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:48:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:48:10.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:48:11.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:48:11.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:48:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:48:12.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:48:12.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:48:13.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:48:13.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:48:14.175 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:48:14.175 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:48:14.175 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 08:48:14.176 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:48:14.188 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.81 ms, Average inference time: 8.38 ms

2025-08-01 08:48:14.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:48:14.301 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:48:14.421 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch568
2025-08-01 08:48:17.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 568/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 1.525e-05, size: 288, ETA: 0:12:05
2025-08-01 08:48:21.072 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 568/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.5, lr: 1.511e-05, size: 416, ETA: 0:12:01
2025-08-01 08:48:24.421 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 568/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, 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: 1.497e-05, size: 544, ETA: 0:11:58
2025-08-01 08:48:27.816 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 568/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 1.483e-05, size: 448, ETA: 0:11:54
2025-08-01 08:48:31.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 568/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.6, lr: 1.469e-05, size: 480, ETA: 0:11:51
2025-08-01 08:48:34.449 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 568/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 2.7, cls_loss: 0.6, lr: 1.454e-05, size: 448, ETA: 0:11:48
2025-08-01 08:48:35.874 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:48:42.706 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:48:43.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:48:43.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4401
2025-08-01 08:48:43.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3759
2025-08-01 08:48:43.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2172
2025-08-01 08:48:43.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3444
2025-08-01 08:48:43.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:48:43.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:48:43.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-08-01 08:48:43.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:48:43.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:48:43.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:48:44.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:48:44.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:48:45.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:48:45.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:48:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:48:46.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:48:47.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:48:47.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:48:48.175 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:48:48.176 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:48:48.176 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 08:48:48.176 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:48:48.182 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.81 ms, Average inference time: 8.34 ms

2025-08-01 08:48:48.183 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:48:48.262 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:48:48.346 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch569
2025-08-01 08:48:51.601 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 569/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.434e-05, size: 480, ETA: 0:11:43
2025-08-01 08:48:55.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 569/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.420e-05, size: 320, ETA: 0:11:39
2025-08-01 08:48:58.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 569/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 1.406e-05, size: 480, ETA: 0:11:36
2025-08-01 08:49:02.014 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 569/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 1.393e-05, size: 384, ETA: 0:11:32
2025-08-01 08:49:05.431 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 569/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 1.379e-05, size: 352, ETA: 0:11:29
2025-08-01 08:49:08.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 569/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.8, lr: 1.365e-05, size: 256, ETA: 0:11:25
2025-08-01 08:49:10.401 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:49:17.183 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:49:17.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:49:18.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3115
2025-08-01 08:49:18.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2886
2025-08-01 08:49:18.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1481
2025-08-01 08:49:18.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2494
2025-08-01 08:49:18.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:49:18.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:49:18.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-01 08:49:18.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.148
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.249
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:49:18.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:49:18.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:49:18.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:49:19.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:49:19.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:49:20.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:49:20.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:49:21.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:49:21.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:49:21.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:49:22.375 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:49:22.375 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-08-01 08:49:22.375 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-08-01 08:49:22.375 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:49:22.382 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.86 ms, Average inference time: 8.38 ms

2025-08-01 08:49:22.383 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:49:22.461 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:49:22.592 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch570
2025-08-01 08:49:26.020 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 570/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.8, lr: 1.346e-05, size: 256, ETA: 0:11:20
2025-08-01 08:49:29.557 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 570/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 1.332e-05, size: 512, ETA: 0:11:17
2025-08-01 08:49:33.045 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 570/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.170s, 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: 1.319e-05, size: 448, ETA: 0:11:14
2025-08-01 08:49:36.309 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 570/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 1.306e-05, size: 320, ETA: 0:11:10
2025-08-01 08:49:39.711 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 570/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 1.292e-05, size: 320, ETA: 0:11:07
2025-08-01 08:49:42.914 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 570/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 1.279e-05, size: 288, ETA: 0:11:03
2025-08-01 08:49:44.331 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:49:51.067 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:49:51.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:49:52.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3906
2025-08-01 08:49:52.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2756
2025-08-01 08:49:52.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1809
2025-08-01 08:49:52.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2824
2025-08-01 08:49:52.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:49:52.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:49:52.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-01 08:49:52.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-08-01 08:49:52.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-08-01 08:49:52.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.282
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:49:52.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:49:53.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:49:53.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:49:54.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:49:54.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:49:55.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:49:56.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:49:56.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:49:57.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:49:57.912 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:49:57.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 08:49:57.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 08:49:57.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:49:57.920 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.89 ms, Average inference time: 8.37 ms

2025-08-01 08:49:57.921 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:49:58.007 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:49:58.090 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch571
2025-08-01 08:50:01.187 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 571/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.260e-05, size: 416, ETA: 0:10:58
2025-08-01 08:50:04.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 571/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.247e-05, size: 480, ETA: 0:10:55
2025-08-01 08:50:07.781 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 571/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.234e-05, size: 416, ETA: 0:10:51
2025-08-01 08:50:10.963 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 571/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.221e-05, size: 448, ETA: 0:10:48
2025-08-01 08:50:14.434 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 571/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 1.208e-05, size: 576, ETA: 0:10:45
2025-08-01 08:50:17.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 571/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 1.196e-05, size: 480, ETA: 0:10:41
2025-08-01 08:50:19.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:50:25.849 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:50:26.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:50:27.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5052
2025-08-01 08:50:27.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3471
2025-08-01 08:50:27.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2581
2025-08-01 08:50:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3701
2025-08-01 08:50:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:50:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:50:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-01 08:50:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-01 08:50:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-01 08:50:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-01 08:50:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:50:27.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:50:27.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:50:27.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:50:27.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:50:27.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:50:27.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:50:27.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:50:27.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:50:28.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:50:29.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:50:29.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:50:30.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:50:31.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:50:31.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:50:32.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:50:33.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:50:34.152 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:50:34.152 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 08:50:34.152 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 08:50:34.152 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:50:34.159 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.88 ms, Average inference time: 8.26 ms

2025-08-01 08:50:34.161 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:50:34.239 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:50:34.315 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch572
2025-08-01 08:50:37.458 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 572/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.177e-05, size: 256, ETA: 0:10:36
2025-08-01 08:50:40.847 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 572/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, 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: 1.165e-05, size: 480, ETA: 0:10:33
2025-08-01 08:50:44.032 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 572/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 1.152e-05, size: 288, ETA: 0:10:29
2025-08-01 08:50:47.402 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 572/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 4.1, cls_loss: 0.7, lr: 1.140e-05, size: 416, ETA: 0:10:26
2025-08-01 08:50:50.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 572/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.127e-05, size: 352, ETA: 0:10:22
2025-08-01 08:50:54.142 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 572/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.115e-05, size: 512, ETA: 0:10:19
2025-08-01 08:50:55.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:51:02.476 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:51:03.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:51:03.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3282
2025-08-01 08:51:03.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2610
2025-08-01 08:51:04.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1358
2025-08-01 08:51:04.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2417
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.261
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.136
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.242
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:51:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:51:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:51:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:51:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:51:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:51:04.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:51:05.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:51:06.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:51:06.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:51:07.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:51:08.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:51:09.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:51:09.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:51:10.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:51:10.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 08:51:10.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-08-01 08:51:10.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:51:10.504 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.88 ms, Average inference time: 8.30 ms

2025-08-01 08:51:10.505 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:51:10.582 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:51:10.662 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch573
2025-08-01 08:51:13.852 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 573/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.097e-05, size: 384, ETA: 0:10:14
2025-08-01 08:51:17.123 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 573/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.085e-05, size: 320, ETA: 0:10:11
2025-08-01 08:51:20.389 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 573/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 1.073e-05, size: 288, ETA: 0:10:07
2025-08-01 08:51:23.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 573/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, 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: 1.061e-05, size: 480, ETA: 0:10:04
2025-08-01 08:51:27.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 573/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 1.049e-05, size: 416, ETA: 0:10:00
2025-08-01 08:51:30.502 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 573/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 1.037e-05, size: 480, ETA: 0:09:57
2025-08-01 08:51:32.011 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:51:38.846 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:51:39.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:51:40.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3683
2025-08-01 08:51:40.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3201
2025-08-01 08:51:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1640
2025-08-01 08:51:40.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2841
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.164
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.284
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:51:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:51:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:51:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:51:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:51:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:51:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:51:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:51:41.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:51:42.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:51:43.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:51:43.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:51:44.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:51:45.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:51:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:51:47.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:51:48.142 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:51:48.142 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:51:48.142 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 08:51:48.143 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:51:48.159 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.89 ms, Average inference time: 8.34 ms

2025-08-01 08:51:48.160 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:51:48.275 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:51:48.418 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch574
2025-08-01 08:51:51.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 574/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.020e-05, size: 512, ETA: 0:09:52
2025-08-01 08:51:55.083 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 574/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.008e-05, size: 576, ETA: 0:09:49
2025-08-01 08:51:58.573 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 574/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 9.965e-06, size: 576, ETA: 0:09:45
2025-08-01 08:52:01.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 574/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 9.849e-06, size: 256, ETA: 0:09:42
2025-08-01 08:52:05.281 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 574/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 9.734e-06, size: 416, ETA: 0:09:38
2025-08-01 08:52:08.999 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 574/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 9.619e-06, size: 512, ETA: 0:09:35
2025-08-01 08:52:10.421 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:52:17.142 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:52:17.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:52:18.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4039
2025-08-01 08:52:18.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3328
2025-08-01 08:52:18.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2150
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3172
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.215
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.317
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:52:18.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:52:18.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:52:18.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:52:18.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:52:18.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:52:18.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:52:18.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:52:18.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:52:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:52:19.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:52:20.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:52:20.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:52:21.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:52:22.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:52:22.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:52:23.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:52:23.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:52:23.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:52:23.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 08:52:23.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:52:23.826 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.85 ms, Average inference time: 8.30 ms

2025-08-01 08:52:23.828 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:52:23.913 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:52:23.996 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch575
2025-08-01 08:52:27.105 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 575/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 9.454e-06, size: 448, ETA: 0:09:30
2025-08-01 08:52:30.323 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 575/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 1.0, lr: 9.341e-06, size: 352, ETA: 0:09:26
2025-08-01 08:52:33.818 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 575/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 10.2, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 5.5, cls_loss: 0.7, lr: 9.229e-06, size: 288, ETA: 0:09:23
2025-08-01 08:52:37.223 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 575/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 1.2, lr: 9.118e-06, size: 512, ETA: 0:09:20
2025-08-01 08:52:40.922 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 575/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 9.007e-06, size: 416, ETA: 0:09:16
2025-08-01 08:52:44.458 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 575/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 8.897e-06, size: 512, ETA: 0:09:13
2025-08-01 08:52:46.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:52:53.020 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:52:53.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:52:54.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3692
2025-08-01 08:52:54.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2424
2025-08-01 08:52:54.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1383
2025-08-01 08:52:54.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2500
2025-08-01 08:52:54.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:52:54.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:52:54.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-01 08:52:54.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-08-01 08:52:54.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.138
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.250
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:52:54.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:52:55.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:52:56.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:52:56.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:52:57.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:52:58.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:52:59.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:52:59.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:53:00.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:53:01.350 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:53:01.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-08-01 08:53:01.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-08-01 08:53:01.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:53:01.361 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.85 ms, Average inference time: 8.32 ms

2025-08-01 08:53:01.361 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:53:01.483 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:53:01.574 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch576
2025-08-01 08:53:04.778 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 576/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 8.738e-06, size: 448, ETA: 0:09:08
2025-08-01 08:53:08.124 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 576/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, data_time: 0.005s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 8.629e-06, size: 416, ETA: 0:09:04
2025-08-01 08:53:11.610 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 576/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.5, lr: 8.521e-06, size: 320, ETA: 0:09:01
2025-08-01 08:53:14.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 576/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 8.414e-06, size: 384, ETA: 0:08:57
2025-08-01 08:53:18.277 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 576/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 8.308e-06, size: 544, ETA: 0:08:54
2025-08-01 08:53:21.995 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 576/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 8.202e-06, size: 576, ETA: 0:08:51
2025-08-01 08:53:23.493 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:53:30.253 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:53:31.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:53:32.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3908
2025-08-01 08:53:32.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2437
2025-08-01 08:53:32.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1852
2025-08-01 08:53:32.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2732
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.185
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.273
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:53:32.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:53:32.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:53:32.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:53:32.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:53:32.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:53:32.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:53:33.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:53:33.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:53:34.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:53:35.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:53:36.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:53:37.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:53:38.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:53:38.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:53:39.828 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:53:39.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:53:39.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-08-01 08:53:39.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:53:39.838 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.84 ms, Average inference time: 8.31 ms

2025-08-01 08:53:39.839 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:53:39.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:53:40.071 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch577
2025-08-01 08:53:43.162 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 577/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.149s, 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: 8.050e-06, size: 480, ETA: 0:08:46
2025-08-01 08:53:46.626 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 577/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.8, lr: 7.945e-06, size: 288, ETA: 0:08:42
2025-08-01 08:53:49.780 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 577/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 7.842e-06, size: 544, ETA: 0:08:39
2025-08-01 08:53:53.157 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 577/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, 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: 7.739e-06, size: 384, ETA: 0:08:35
2025-08-01 08:53:56.613 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 577/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.172s, 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: 7.637e-06, size: 512, ETA: 0:08:32
2025-08-01 08:53:59.978 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 577/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 7.535e-06, size: 288, ETA: 0:08:29
2025-08-01 08:54:01.528 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:54:08.357 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:54:08.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:54:09.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4239
2025-08-01 08:54:09.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3558
2025-08-01 08:54:09.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2026
2025-08-01 08:54:09.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3274
2025-08-01 08:54:09.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.203
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.327
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:54:09.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:54:09.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:54:09.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:54:10.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:54:10.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:54:11.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:54:11.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:54:11.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:54:12.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:54:12.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:54:13.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:54:13.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:54:13.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-08-01 08:54:13.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:54:13.172 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.80 ms, Average inference time: 8.28 ms

2025-08-01 08:54:13.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:54:13.255 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:54:13.333 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch578
2025-08-01 08:54:16.681 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 578/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, 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: 7.389e-06, size: 384, ETA: 0:08:24
2025-08-01 08:54:20.036 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 578/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 7.290e-06, size: 576, ETA: 0:08:20
2025-08-01 08:54:23.422 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 578/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 7.190e-06, size: 448, ETA: 0:08:17
2025-08-01 08:54:26.787 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 578/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 7.092e-06, size: 576, ETA: 0:08:13
2025-08-01 08:54:30.338 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 578/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 6.994e-06, size: 544, ETA: 0:08:10
2025-08-01 08:54:33.749 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 578/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.4Gb, iter_time: 0.170s, 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: 6.897e-06, size: 544, ETA: 0:08:06
2025-08-01 08:54:35.301 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:54:42.168 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:54:42.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:54:43.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3947
2025-08-01 08:54:43.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2816
2025-08-01 08:54:43.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1672
2025-08-01 08:54:43.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2812
2025-08-01 08:54:43.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:54:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:54:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-01 08:54:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.282
2025-08-01 08:54:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.167
2025-08-01 08:54:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.281
2025-08-01 08:54:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:54:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:54:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:54:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:54:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:54:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:54:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:54:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:54:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:54:44.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:54:44.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:54:45.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:54:46.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:54:46.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:54:47.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:54:48.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:54:48.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:54:49.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:54:49.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-01 08:54:49.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 08:54:49.403 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:54:49.411 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.64 ms, Average NMS time: 0.84 ms, Average inference time: 8.48 ms

2025-08-01 08:54:49.412 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:54:49.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:54:49.660 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch579
2025-08-01 08:54:52.714 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 579/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 3.7, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.2, cls_loss: 0.5, lr: 6.757e-06, size: 416, ETA: 0:08:01
2025-08-01 08:54:56.014 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 579/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 1.0, lr: 6.662e-06, size: 320, ETA: 0:07:58
2025-08-01 08:54:59.212 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 579/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 6.567e-06, size: 416, ETA: 0:07:55
2025-08-01 08:55:02.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 579/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 6.473e-06, size: 320, ETA: 0:07:51
2025-08-01 08:55:06.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 579/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 6.380e-06, size: 416, ETA: 0:07:48
2025-08-01 08:55:09.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 579/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 6.287e-06, size: 448, ETA: 0:07:44
2025-08-01 08:55:10.773 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:55:17.482 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:55:18.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:55:18.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4813
2025-08-01 08:55:18.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4389
2025-08-01 08:55:18.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2346
2025-08-01 08:55:18.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3849
2025-08-01 08:55:18.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:55:18.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:55:18.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-01 08:55:18.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 08:55:18.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-08-01 08:55:18.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.385
2025-08-01 08:55:18.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:55:18.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:55:18.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:55:18.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:55:18.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:55:18.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:55:18.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:55:18.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:55:18.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:55:19.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:55:20.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:55:20.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:55:21.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:55:22.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:55:22.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:55:23.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:55:23.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:55:24.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:55:24.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:55:24.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:55:24.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:55:24.504 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.85 ms, Average inference time: 8.36 ms

2025-08-01 08:55:24.505 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:55:24.585 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:55:24.665 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch580
2025-08-01 08:55:28.033 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 580/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, 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: 6.153e-06, size: 576, ETA: 0:07:39
2025-08-01 08:55:31.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 580/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 5.1, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 2.4, cls_loss: 0.5, lr: 6.062e-06, size: 384, ETA: 0:07:36
2025-08-01 08:55:34.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 580/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.6, lr: 5.972e-06, size: 480, ETA: 0:07:33
2025-08-01 08:55:38.043 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 580/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 3.2, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 5.882e-06, size: 320, ETA: 0:07:29
2025-08-01 08:55:41.136 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 580/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 5.793e-06, size: 320, ETA: 0:07:26
2025-08-01 08:55:44.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 580/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 5.705e-06, size: 512, ETA: 0:07:22
2025-08-01 08:55:46.131 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:55:52.864 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:55:53.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:55:53.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4781
2025-08-01 08:55:54.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4342
2025-08-01 08:55:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2141
2025-08-01 08:55:54.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3755
2025-08-01 08:55:54.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:55:54.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:55:54.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-01 08:55:54.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.214
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.375
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:55:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:55:54.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:55:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:55:55.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:55:55.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:55:56.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:55:57.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:55:57.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:55:58.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:55:58.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:55:59.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:55:59.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:55:59.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:55:59.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:55:59.581 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.83 ms, Average inference time: 8.32 ms

2025-08-01 08:55:59.582 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:55:59.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:55:59.808 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch581
2025-08-01 08:56:03.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 581/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 5.578e-06, size: 448, ETA: 0:07:17
2025-08-01 08:56:06.324 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 581/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 5.491e-06, size: 320, ETA: 0:07:14
2025-08-01 08:56:09.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 581/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.7, lr: 5.405e-06, size: 384, ETA: 0:07:10
2025-08-01 08:56:13.123 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 581/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 5.320e-06, size: 512, ETA: 0:07:07
2025-08-01 08:56:16.482 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 581/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 9.2, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 4.4, cls_loss: 1.5, lr: 5.235e-06, size: 544, ETA: 0:07:04
2025-08-01 08:56:19.866 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 581/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 5.151e-06, size: 544, ETA: 0:07:00
2025-08-01 08:56:21.256 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:56:28.054 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:56:28.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:56:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3849
2025-08-01 08:56:29.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2956
2025-08-01 08:56:29.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1730
2025-08-01 08:56:29.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2845
2025-08-01 08:56:29.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.173
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.285
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:56:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:56:29.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:56:29.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:56:29.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:56:29.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:56:30.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:56:30.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:56:31.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:56:32.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:56:33.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:56:33.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:56:34.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:56:35.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:56:35.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:56:35.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 08:56:35.935 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 08:56:35.935 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:56:35.943 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.85 ms, Average inference time: 8.39 ms

2025-08-01 08:56:35.944 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:56:36.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:56:36.133 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch582
2025-08-01 08:56:39.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 582/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.5, lr: 5.030e-06, size: 448, ETA: 0:06:55
2025-08-01 08:56:42.991 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 582/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 4.948e-06, size: 544, ETA: 0:06:52
2025-08-01 08:56:46.405 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 582/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.5, lr: 4.866e-06, size: 544, ETA: 0:06:48
2025-08-01 08:56:49.600 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 582/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.9, lr: 4.785e-06, size: 320, ETA: 0:06:45
2025-08-01 08:56:52.713 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 582/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.154s, 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: 4.705e-06, size: 256, ETA: 0:06:41
2025-08-01 08:56:56.200 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 582/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 4.625e-06, size: 480, ETA: 0:06:38
2025-08-01 08:56:57.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:57:04.353 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:57:04.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:57:05.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4774
2025-08-01 08:57:05.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4359
2025-08-01 08:57:05.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2132
2025-08-01 08:57:05.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3755
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:57:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:57:05.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:57:05.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:57:05.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:57:05.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:57:05.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:57:05.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:57:05.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:57:06.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:57:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:57:07.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:57:07.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:57:08.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:57:08.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:57:09.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:57:10.074 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:57:10.074 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 08:57:10.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:57:10.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:57:10.082 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.82 ms, Average inference time: 8.42 ms

2025-08-01 08:57:10.083 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:57:10.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:57:10.289 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch583
2025-08-01 08:57:13.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 583/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 4.511e-06, size: 512, ETA: 0:06:33
2025-08-01 08:57:16.992 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 583/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 4.433e-06, size: 512, ETA: 0:06:30
2025-08-01 08:57:20.323 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 583/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 4.356e-06, size: 384, ETA: 0:06:26
2025-08-01 08:57:23.561 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 583/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.157s, 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: 4.279e-06, size: 384, ETA: 0:06:23
2025-08-01 08:57:26.916 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 583/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 4.203e-06, size: 576, ETA: 0:06:19
2025-08-01 08:57:30.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 583/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.0, cls_loss: 0.6, lr: 4.128e-06, size: 544, ETA: 0:06:16
2025-08-01 08:57:31.748 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:57:38.582 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:57:39.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:57:39.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4761
2025-08-01 08:57:40.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4445
2025-08-01 08:57:40.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2302
2025-08-01 08:57:40.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3836
2025-08-01 08:57:40.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:57:40.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:57:40.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.230
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.384
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:57:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:57:40.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:57:40.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:57:41.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:57:41.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:57:42.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:57:42.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:57:43.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:57:43.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:57:44.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:57:44.729 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:57:44.729 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:57:44.729 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 08:57:44.729 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:57:44.737 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.83 ms, Average inference time: 8.25 ms

2025-08-01 08:57:44.738 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:57:44.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:57:44.906 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch584
2025-08-01 08:57:48.098 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 584/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 4.020e-06, size: 544, ETA: 0:06:11
2025-08-01 08:57:51.691 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 584/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.174s, data_time: 0.004s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 3.946e-06, size: 576, ETA: 0:06:08
2025-08-01 08:57:55.130 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 584/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.8, lr: 3.873e-06, size: 544, ETA: 0:06:04
2025-08-01 08:57:58.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 584/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.9, lr: 3.801e-06, size: 480, ETA: 0:06:01
2025-08-01 08:58:01.914 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 584/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 3.730e-06, size: 320, ETA: 0:05:57
2025-08-01 08:58:05.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 584/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.9, lr: 3.659e-06, size: 256, ETA: 0:05:54
2025-08-01 08:58:06.483 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:58:13.351 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:58:14.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:58:14.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3777
2025-08-01 08:58:14.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3176
2025-08-01 08:58:14.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1305
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2752
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.130
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.275
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:58:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:58:14.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:58:14.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:58:14.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:58:14.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:58:14.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:58:14.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:58:14.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:58:15.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:58:15.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:58:16.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:58:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:58:17.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:58:18.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:58:18.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:58:19.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:58:19.882 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:58:19.882 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-08-01 08:58:19.882 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-01 08:58:19.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:58:19.890 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.84 ms, Average inference time: 8.31 ms

2025-08-01 08:58:19.891 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:58:19.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:58:20.056 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch585
2025-08-01 08:58:23.347 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 585/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 9.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 4.4, cls_loss: 0.7, lr: 3.557e-06, size: 576, ETA: 0:05:49
2025-08-01 08:58:26.826 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 585/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, 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: 3.488e-06, size: 256, ETA: 0:05:46
2025-08-01 08:58:30.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 585/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 3.419e-06, size: 352, ETA: 0:05:42
2025-08-01 08:58:33.497 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 585/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 9.1, iou_loss: 3.7, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 0.9, lr: 3.351e-06, size: 256, ETA: 0:05:39
2025-08-01 08:58:36.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 585/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 3.284e-06, size: 576, ETA: 0:05:35
2025-08-01 08:58:40.384 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 585/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 3.218e-06, size: 448, ETA: 0:05:32
2025-08-01 08:58:41.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:58:48.663 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:58:49.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:58:49.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4392
2025-08-01 08:58:49.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3992
2025-08-01 08:58:49.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1932
2025-08-01 08:58:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3438
2025-08-01 08:58:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:58:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:58:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 08:58:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-01 08:58:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-08-01 08:58:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-08-01 08:58:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:58:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:58:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:58:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:58:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:58:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:58:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:58:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:58:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:58:50.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:58:50.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:58:51.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:58:51.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:58:52.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:58:52.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:58:53.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:58:54.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:58:54.515 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:58:54.515 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 08:58:54.515 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 08:58:54.516 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:58:54.522 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.85 ms, Average inference time: 8.34 ms

2025-08-01 08:58:54.523 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:58:54.601 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:58:54.680 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch586
2025-08-01 08:58:57.879 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 586/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 3.122e-06, size: 352, ETA: 0:05:27
2025-08-01 08:59:01.290 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 586/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 3.058e-06, size: 512, ETA: 0:05:23
2025-08-01 08:59:04.614 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 586/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 2.993e-06, size: 256, ETA: 0:05:20
2025-08-01 08:59:07.986 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 586/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 2.930e-06, size: 352, ETA: 0:05:17
2025-08-01 08:59:11.296 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 586/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.5, lr: 2.867e-06, size: 512, ETA: 0:05:13
2025-08-01 08:59:14.687 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 586/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.7, lr: 2.805e-06, size: 352, ETA: 0:05:10
2025-08-01 08:59:16.298 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:59:23.309 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 08:59:24.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 08:59:25.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5007
2025-08-01 08:59:25.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4435
2025-08-01 08:59:25.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2924
2025-08-01 08:59:25.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4122
2025-08-01 08:59:25.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 08:59:25.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 08:59:25.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-01 08:59:25.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-01 08:59:25.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-08-01 08:59:25.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-08-01 08:59:25.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 08:59:25.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 08:59:25.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 08:59:25.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 08:59:25.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 08:59:25.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 08:59:25.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 08:59:25.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 08:59:25.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 08:59:26.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 08:59:27.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 08:59:28.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 08:59:29.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 08:59:30.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 08:59:31.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 08:59:32.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 08:59:33.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 08:59:33.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 08:59:33.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-01 08:59:33.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 08:59:33.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 08:59:33.977 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.84 ms, Average inference time: 8.17 ms

2025-08-01 08:59:33.978 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:59:34.054 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 08:59:34.138 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch587
2025-08-01 08:59:37.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 587/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 2.716e-06, size: 512, ETA: 0:05:05
2025-08-01 08:59:40.894 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 587/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.174s, data_time: 0.004s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 2.656e-06, size: 384, ETA: 0:05:01
2025-08-01 08:59:44.233 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 587/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.6, lr: 2.596e-06, size: 480, ETA: 0:04:58
2025-08-01 08:59:47.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 587/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 2.537e-06, size: 448, ETA: 0:04:55
2025-08-01 08:59:51.085 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 587/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 2.478e-06, size: 480, ETA: 0:04:51
2025-08-01 08:59:54.416 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 587/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 2.421e-06, size: 576, ETA: 0:04:48
2025-08-01 08:59:56.096 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:00:02.963 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:00:03.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:00:03.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4929
2025-08-01 09:00:04.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4586
2025-08-01 09:00:04.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2667
2025-08-01 09:00:04.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4061
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.406
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:00:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:00:04.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:00:04.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:00:04.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:00:04.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:00:04.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:00:04.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:00:04.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:00:05.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:00:05.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:00:06.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:00:06.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:00:07.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:00:07.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:00:08.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:00:08.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:00:08.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-01 09:00:08.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-01 09:00:08.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:00:08.535 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.82 ms, Average inference time: 8.27 ms

2025-08-01 09:00:08.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:00:08.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:00:08.747 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch588
2025-08-01 09:00:12.351 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 588/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 2.338e-06, size: 384, ETA: 0:04:43
2025-08-01 09:00:15.677 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 588/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, 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: 2.282e-06, size: 448, ETA: 0:04:39
2025-08-01 09:00:18.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 588/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 2.227e-06, size: 448, ETA: 0:04:36
2025-08-01 09:00:22.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 588/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 2.172e-06, size: 384, ETA: 0:04:32
2025-08-01 09:00:25.542 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 588/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 2.118e-06, size: 480, ETA: 0:04:29
2025-08-01 09:00:28.864 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 588/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 3.3, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 2.064e-06, size: 352, ETA: 0:04:26
2025-08-01 09:00:30.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:00:37.159 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:00:37.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:00:38.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4372
2025-08-01 09:00:38.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4020
2025-08-01 09:00:38.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2428
2025-08-01 09:00:38.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3607
2025-08-01 09:00:38.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:00:38.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:00:38.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 09:00:38.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 09:00:38.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.243
2025-08-01 09:00:38.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-08-01 09:00:38.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:00:38.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:00:38.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:00:38.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:00:38.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:00:38.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:00:38.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:00:38.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:00:38.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:00:39.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:00:39.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:00:40.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:00:41.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:00:41.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:00:42.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:00:43.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:00:44.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:00:44.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:00:44.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 09:00:44.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 09:00:44.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:00:44.739 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.85 ms, Average inference time: 8.41 ms

2025-08-01 09:00:44.741 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:00:44.852 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:00:44.957 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch589
2025-08-01 09:00:48.161 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 589/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 1.988e-06, size: 448, ETA: 0:04:21
2025-08-01 09:00:51.650 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 589/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.172s, data_time: 0.005s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.937e-06, size: 512, ETA: 0:04:17
2025-08-01 09:00:55.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 589/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.886e-06, size: 416, ETA: 0:04:14
2025-08-01 09:00:58.473 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 589/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.835e-06, size: 256, ETA: 0:04:10
2025-08-01 09:01:01.575 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 589/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.150s, 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: 1.786e-06, size: 416, ETA: 0:04:07
2025-08-01 09:01:04.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 589/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 1.737e-06, size: 352, ETA: 0:04:04
2025-08-01 09:01:06.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:01:13.165 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:01:13.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:01:14.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4959
2025-08-01 09:01:14.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4368
2025-08-01 09:01:14.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1949
2025-08-01 09:01:14.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3758
2025-08-01 09:01:14.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:01:14.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:01:14.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:01:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:01:14.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:01:14.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:01:15.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:01:15.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:01:16.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:01:17.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:01:17.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:01:18.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:01:18.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:01:19.316 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:01:19.316 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 09:01:19.316 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-01 09:01:19.317 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:01:19.326 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.81 ms, Average inference time: 8.32 ms

2025-08-01 09:01:19.326 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:01:19.446 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:01:19.530 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch590
2025-08-01 09:01:22.873 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 590/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.667e-06, size: 448, ETA: 0:03:59
2025-08-01 09:01:26.286 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 590/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, 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: 1.620e-06, size: 544, ETA: 0:03:55
2025-08-01 09:01:29.753 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 590/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 1.573e-06, size: 576, ETA: 0:03:52
2025-08-01 09:01:33.286 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 590/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.527e-06, size: 448, ETA: 0:03:48
2025-08-01 09:01:36.619 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 590/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 1.482e-06, size: 384, ETA: 0:03:45
2025-08-01 09:01:39.910 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 590/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.437e-06, size: 448, ETA: 0:03:41
2025-08-01 09:01:41.362 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:01:48.133 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:01:48.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:01:49.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4596
2025-08-01 09:01:49.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4108
2025-08-01 09:01:49.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2230
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3645
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.364
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:01:49.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:01:49.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:01:49.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:01:49.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:01:49.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:01:49.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:01:49.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:01:50.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:01:50.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:01:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:01:52.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:01:52.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:01:53.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:01:53.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:01:54.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:01:55.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:01:55.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 09:01:55.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 09:01:55.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:01:55.114 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.87 ms, Average inference time: 8.48 ms

2025-08-01 09:01:55.117 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:01:55.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:01:55.288 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch591
2025-08-01 09:01:58.469 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 591/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.374e-06, size: 512, ETA: 0:03:37
2025-08-01 09:02:02.035 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 591/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.176s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.331e-06, size: 288, ETA: 0:03:33
2025-08-01 09:02:05.316 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 591/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 1.289e-06, size: 352, ETA: 0:03:30
2025-08-01 09:02:08.521 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 591/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.247e-06, size: 320, ETA: 0:03:26
2025-08-01 09:02:12.047 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 591/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.206e-06, size: 352, ETA: 0:03:23
2025-08-01 09:02:15.319 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 591/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.166e-06, size: 320, ETA: 0:03:19
2025-08-01 09:02:16.693 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:02:23.472 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:02:24.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:02:25.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4581
2025-08-01 09:02:25.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4022
2025-08-01 09:02:25.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2497
2025-08-01 09:02:25.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3700
2025-08-01 09:02:25.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:02:25.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.250
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:02:25.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:02:25.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:02:26.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:02:27.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:02:28.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:02:29.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:02:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:02:31.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:02:32.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:02:33.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:02:34.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:02:34.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-01 09:02:34.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 09:02:34.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:02:34.798 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.91 ms, Average inference time: 8.34 ms

2025-08-01 09:02:34.799 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:02:34.881 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:02:34.960 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch592
2025-08-01 09:02:38.063 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 592/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.150s, 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: 1.109e-06, size: 352, ETA: 0:03:14
2025-08-01 09:02:41.463 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 592/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, 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: 1.070e-06, size: 480, ETA: 0:03:11
2025-08-01 09:02:45.071 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 592/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.032e-06, size: 352, ETA: 0:03:08
2025-08-01 09:02:48.476 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 592/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, 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: 9.953e-07, size: 320, ETA: 0:03:04
2025-08-01 09:02:51.816 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 592/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 9.588e-07, size: 576, ETA: 0:03:01
2025-08-01 09:02:55.153 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 592/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.8, lr: 9.230e-07, size: 320, ETA: 0:02:57
2025-08-01 09:02:56.658 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:03:03.344 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:03:04.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:03:04.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4346
2025-08-01 09:03:04.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4025
2025-08-01 09:03:04.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1869
2025-08-01 09:03:04.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3413
2025-08-01 09:03:04.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:03:04.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:03:04.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-08-01 09:03:04.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-01 09:03:04.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.187
2025-08-01 09:03:04.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.341
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:03:04.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:03:05.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:03:05.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:03:06.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:03:06.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:03:07.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:03:07.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:03:08.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:03:08.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:03:09.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:03:09.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 09:03:09.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 09:03:09.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:03:09.396 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.85 ms, Average inference time: 8.35 ms

2025-08-01 09:03:09.398 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:03:09.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:03:09.557 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch593
2025-08-01 09:03:12.861 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 593/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, 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: 8.723e-07, size: 352, ETA: 0:02:52
2025-08-01 09:03:16.176 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 593/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 8.382e-07, size: 512, ETA: 0:02:49
2025-08-01 09:03:19.423 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 593/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.8, lr: 8.047e-07, size: 256, ETA: 0:02:46
2025-08-01 09:03:22.529 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 593/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.8, lr: 7.720e-07, size: 256, ETA: 0:02:42
2025-08-01 09:03:26.080 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 593/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.174s, 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: 7.399e-07, size: 448, ETA: 0:02:39
2025-08-01 09:03:29.393 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 593/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 7.085e-07, size: 256, ETA: 0:02:35
2025-08-01 09:03:31.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:03:38.022 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:03:38.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:03:39.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4077
2025-08-01 09:03:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3641
2025-08-01 09:03:39.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1662
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3127
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.166
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:03:39.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:03:39.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:03:39.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:03:39.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:03:39.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:03:39.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:03:39.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:03:39.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:03:40.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:03:41.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:03:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:03:42.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:03:42.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:03:43.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:03:44.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:03:44.815 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:03:44.815 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 09:03:44.815 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 09:03:44.815 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:03:44.824 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.84 ms, Average inference time: 8.26 ms

2025-08-01 09:03:44.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:03:44.940 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:03:45.023 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch594
2025-08-01 09:03:48.522 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 594/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 6.641e-07, size: 480, ETA: 0:02:30
2025-08-01 09:03:51.953 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 594/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 6.344e-07, size: 512, ETA: 0:02:27
2025-08-01 09:03:55.461 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 594/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 6.053e-07, size: 512, ETA: 0:02:24
2025-08-01 09:03:58.807 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 594/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 5.770e-07, size: 288, ETA: 0:02:20
2025-08-01 09:04:02.300 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 594/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 5.493e-07, size: 544, ETA: 0:02:17
2025-08-01 09:04:05.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 594/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 5.222e-07, size: 480, ETA: 0:02:13
2025-08-01 09:04:07.315 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:04:14.057 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:04:14.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:04:14.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4430
2025-08-01 09:04:15.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4295
2025-08-01 09:04:15.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2331
2025-08-01 09:04:15.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3685
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.443
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.233
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:04:15.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:04:15.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:04:15.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:04:15.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:04:15.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:04:15.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:04:15.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:04:15.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:04:16.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:04:16.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:04:16.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:04:17.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:04:17.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:04:18.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:04:18.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:04:18.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:04:18.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 09:04:18.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-01 09:04:18.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:04:18.960 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.81 ms, Average inference time: 8.34 ms

2025-08-01 09:04:18.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:04:19.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:04:19.171 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch595
2025-08-01 09:04:22.453 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 595/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.8, lr: 4.843e-07, size: 352, ETA: 0:02:08
2025-08-01 09:04:25.889 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 595/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.5, lr: 4.589e-07, size: 448, ETA: 0:02:05
2025-08-01 09:04:29.173 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 595/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.7, lr: 4.343e-07, size: 320, ETA: 0:02:01
2025-08-01 09:04:32.517 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 595/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 4.103e-07, size: 480, ETA: 0:01:58
2025-08-01 09:04:35.855 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 595/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 3.870e-07, size: 320, ETA: 0:01:55
2025-08-01 09:04:39.202 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 595/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 3.643e-07, size: 256, ETA: 0:01:51
2025-08-01 09:04:40.759 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:04:47.562 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:04:48.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:04:48.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4159
2025-08-01 09:04:48.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3339
2025-08-01 09:04:48.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1692
2025-08-01 09:04:48.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3063
2025-08-01 09:04:48.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:04:48.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:04:48.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-01 09:04:48.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-01 09:04:48.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.306
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:04:48.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:04:48.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:04:49.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:04:49.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:04:50.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:04:51.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:04:51.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:04:52.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:04:52.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:04:53.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:04:53.813 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:04:53.813 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-01 09:04:53.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-08-01 09:04:53.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:04:53.823 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.84 ms, Average inference time: 8.38 ms

2025-08-01 09:04:53.823 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:04:53.935 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:04:54.049 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch596
2025-08-01 09:04:57.483 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 596/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 3.328e-07, size: 416, ETA: 0:01:46
2025-08-01 09:05:01.108 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 596/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 3.118e-07, size: 352, ETA: 0:01:43
2025-08-01 09:05:04.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 596/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.174s, 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: 2.915e-07, size: 416, ETA: 0:01:39
2025-08-01 09:05:08.026 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 596/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 2.719e-07, size: 576, ETA: 0:01:36
2025-08-01 09:05:11.526 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 596/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 2.530e-07, size: 416, ETA: 0:01:33
2025-08-01 09:05:15.024 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 596/600, iter: 120/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.168s, 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: 2.348e-07, size: 256, ETA: 0:01:29
2025-08-01 09:05:16.602 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:05:23.429 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:05:24.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:05:24.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4446
2025-08-01 09:05:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3528
2025-08-01 09:05:24.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2283
2025-08-01 09:05:24.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3419
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.228
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.342
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:05:24.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:05:24.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:05:24.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:05:24.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:05:24.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:05:24.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:05:24.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:05:24.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:05:25.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:05:25.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:05:26.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:05:26.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:05:27.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:05:27.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:05:28.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:05:28.523 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:05:28.524 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 09:05:28.524 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 09:05:28.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:05:28.538 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.84 ms, Average inference time: 8.39 ms

2025-08-01 09:05:28.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:05:28.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:05:28.807 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch597
2025-08-01 09:05:32.187 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 597/600, iter: 20/129, gpu mem: 2082Mb, mem: 77.9Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 2.096e-07, size: 480, ETA: 0:01:24
2025-08-01 09:05:35.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 597/600, iter: 40/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.930e-07, size: 256, ETA: 0:01:21
2025-08-01 09:05:39.139 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 597/600, iter: 60/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 1.771e-07, size: 544, ETA: 0:01:17
2025-08-01 09:05:42.525 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 597/600, iter: 80/129, gpu mem: 2082Mb, mem: 77.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 2.9, iou_loss: 0.9, l1_loss: 0.3, conf_loss: 1.3, cls_loss: 0.4, lr: 1.619e-07, size: 576, ETA: 0:01:14
2025-08-01 09:05:46.153 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 597/600, iter: 100/129, gpu mem: 2082Mb, mem: 77.8Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.8, lr: 1.474e-07, size: 448, ETA: 0:01:11
2025-08-01 09:05:49.723 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 597/600, iter: 120/129, gpu mem: 2082Mb, mem: 78.1Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.336e-07, size: 576, ETA: 0:01:07
2025-08-01 09:05:51.336 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:05:58.871 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:05:59.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:05:59.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4556
2025-08-01 09:05:59.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4281
2025-08-01 09:05:59.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2018
2025-08-01 09:05:59.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3618
2025-08-01 09:05:59.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.362
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:05:59.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:05:59.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:05:59.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:05:59.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:05:59.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:05:59.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:06:00.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:06:01.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:06:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:06:02.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:06:02.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:06:02.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:06:03.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:06:03.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:06:04.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:06:04.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 09:06:04.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-01 09:06:04.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:06:04.463 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.91 ms, Average NMS time: 0.84 ms, Average inference time: 8.75 ms

2025-08-01 09:06:04.464 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:06:04.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:06:04.626 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch598
2025-08-01 09:06:07.969 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 598/600, iter: 20/129, gpu mem: 2082Mb, mem: 94.1Gb, iter_time: 0.164s, 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: 1.147e-07, size: 448, ETA: 0:01:02
2025-08-01 09:06:11.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 598/600, iter: 40/129, gpu mem: 2082Mb, mem: 94.1Gb, iter_time: 0.177s, data_time: 0.004s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.026e-07, size: 544, ETA: 0:00:59
2025-08-01 09:06:15.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 598/600, iter: 60/129, gpu mem: 2082Mb, mem: 94.1Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 9.109e-08, size: 352, ETA: 0:00:55
2025-08-01 09:06:18.406 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 598/600, iter: 80/129, gpu mem: 2082Mb, mem: 94.0Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 8.029e-08, size: 480, ETA: 0:00:52
2025-08-01 09:06:21.860 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 598/600, iter: 100/129, gpu mem: 2082Mb, mem: 94.0Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 7.017e-08, size: 480, ETA: 0:00:49
2025-08-01 09:06:25.255 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 598/600, iter: 120/129, gpu mem: 2082Mb, mem: 94.0Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 6.073e-08, size: 480, ETA: 0:00:45
2025-08-01 09:06:26.794 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:06:34.492 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:06:35.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:06:35.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4389
2025-08-01 09:06:35.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3038
2025-08-01 09:06:35.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2236
2025-08-01 09:06:35.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3221
2025-08-01 09:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-01 09:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-01 09:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-08-01 09:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-08-01 09:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:06:36.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:06:36.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:06:36.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:06:36.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:06:36.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:06:36.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:06:36.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:06:36.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:06:37.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:06:37.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:06:38.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:06:39.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:06:39.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:06:40.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:06:41.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:06:41.940 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:06:41.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-01 09:06:41.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-08-01 09:06:41.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:06:41.951 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.81 ms, Average NMS time: 0.84 ms, Average inference time: 8.65 ms

2025-08-01 09:06:41.951 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:06:42.037 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:06:42.122 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch599
2025-08-01 09:06:45.577 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 599/600, iter: 20/129, gpu mem: 2082Mb, mem: 96.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 4.825e-08, size: 416, ETA: 0:00:40
2025-08-01 09:06:49.119 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 599/600, iter: 40/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.176s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 4.049e-08, size: 320, ETA: 0:00:37
2025-08-01 09:06:52.523 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 599/600, iter: 60/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 3.340e-08, size: 480, ETA: 0:00:33
2025-08-01 09:06:55.798 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 599/600, iter: 80/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 2.699e-08, size: 352, ETA: 0:00:30
2025-08-01 09:06:59.426 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 599/600, iter: 100/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 2.127e-08, size: 320, ETA: 0:00:26
2025-08-01 09:07:03.045 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 599/600, iter: 120/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.622e-08, size: 384, ETA: 0:00:23
2025-08-01 09:07:04.533 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:07:14.106 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:07:14.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:07:15.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4322
2025-08-01 09:07:15.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3880
2025-08-01 09:07:15.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2145
2025-08-01 09:07:15.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3449
2025-08-01 09:07:15.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:07:15.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:07:15.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-01 09:07:15.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-01 09:07:15.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.215
2025-08-01 09:07:15.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:07:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:07:16.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:07:16.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:07:17.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:07:18.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:07:19.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:07:19.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:07:20.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:07:21.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:07:21.596 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:07:21.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-01 09:07:21.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-08-01 09:07:21.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:07:21.604 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.11 ms, Average NMS time: 0.79 ms, Average inference time: 7.90 ms

2025-08-01 09:07:21.606 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:07:21.692 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:07:21.775 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch600
2025-08-01 09:07:25.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 600/600, iter: 20/129, gpu mem: 2082Mb, mem: 110.2Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.012e-08, size: 384, ETA: 0:00:18
2025-08-01 09:07:28.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 600/600, iter: 40/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.5, lr: 6.748e-09, size: 384, ETA: 0:00:15
2025-08-01 09:07:31.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 600/600, iter: 60/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.173s, data_time: 0.010s, total_loss: 7.6, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.7, lr: 4.056e-09, size: 512, ETA: 0:00:11
2025-08-01 09:07:35.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 600/600, iter: 80/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 2.045e-09, size: 288, ETA: 0:00:08
2025-08-01 09:07:39.350 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 600/600, iter: 100/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.202s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 7.164e-10, size: 480, ETA: 0:00:04
2025-08-01 09:07:42.978 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 600/600, iter: 120/129, gpu mem: 2082Mb, mem: 110.1Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.6, lr: 6.900e-11, size: 352, ETA: 0:00:01
2025-08-01 09:07:44.720 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:07:58.723 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-01 09:07:59.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-01 09:08:00.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4471
2025-08-01 09:08:00.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3663
2025-08-01 09:08:00.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2229
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3454
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-01 09:08:00.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-01 09:08:00.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-01 09:08:00.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-01 09:08:00.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-01 09:08:00.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-01 09:08:00.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-01 09:08:00.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-01 09:08:00.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-01 09:08:01.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-01 09:08:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-01 09:08:02.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-01 09:08:02.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-01 09:08:03.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-01 09:08:04.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-01 09:08:04.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-01 09:08:05.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-01 09:08:05.695 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-01 09:08:05.695 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-01 09:08:05.695 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-01 09:08:05.695 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-01 09:08:05.702 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.82 ms, Average inference time: 8.13 ms

2025-08-01 09:08:05.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:08:06.208 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_trainset_sc1
2025-08-01 09:08:06.392 | INFO     | yolox_microbt.core.trainer:after_train:172 - Training of experiment is done and the best AP is 30.28
